Muhammad Shahbaz
email: shahbazmohd@live.com

Thi Hong
email: thv.hoang@montpellier-bs.com

Van Hoang

Mantu Kumar
email: mantu65@gmail.com

David Roubaud
email: d.roubaud@montpellier-bs.com

Thi Hong Van Hoang

Mantu Kumar Mahalik

Energy Consumption, Financial Development and Economic Growth in India: New Evidence from a Nonlinear and Asymmetric Analysis

Keywords: Financial Development, Energy, Growth, India, Asymmetries JEL classification: O13

This paper investigates the asymmetric relationship between energy consumption and economic growth by incorporating financial development, capital and labour into a production function covering the Indian economy from 1960Q 1 -2015Q 4 . The nonlinear autoregressive distributed lag bounds testing approach is applied to examine the asymmetric cointegration between the variables. An asymmetric causality test is also employed to examine the causal association between the considered variables. The results indicate cointegration between the variables in the presence of asymmetries. The asymmetric causality results show that only negative shocks in energy consumption have impacts on economic growth. In the same vein, only negative shocks in financial development have impacts on economic growth. By contrast, symmetrically, capital formation causes economic growth. Finally, over the study period, a neutral effect exists between the labour force and economic growth in India. The implications of these results for growth policies in India are also discussed.

Introduction

This study uses quarterly data to investigate the relationship between energy consumption and economic growth in India over the 1960-2015 period. Financial development (measured by domestic credit to the private sector), 1 capital formation and labour force 2 are also included in the Cobb-Douglas production function. This study's main contribution to the existing literature on the energy-growth nexus concerns its examination of the potential asymmetric relationship between the variables. Taking this asymmetry into account is important because a positive or negative variation on one variable does not have the same impact on the other variable. The existence of an asymmetric relationship between two variables can be caused by numerous factors, one of which concerns the complexity of economic systems and the mechanisms that generate the variables under study. This complexity may induce various channels through which one variable affects another. In our case, the relationship between energy consumption and economic growth has been found to be very complex because previous studies have shown four possible impact channels: the growth, conservative, feedback and neutrality hypotheses. 3 On the one hand, the relationship between energy consumption and financial development can also be very complex because numerous impact channels can exist between them. One channel argues that financial development causes economic growth, which in turn causes an increase in energy consumption. On the other hand, financial development can also lead to an increase in investments in new technologies, which allow for the reduction of energy consumption [START_REF] Sadorsky | The impact of financial development on energy consumption in emerging economies[END_REF]. In this context, examining how positive and negative variations of financial development can affect energy consumption is very important. As such, we propose using econometric methods that allow for the consideration of the asymmetry in studying the cointegration and causality between economic growth, financial development and energy consumption, including capital and labour, in India from 1960 to 2015. These methods were recently developed by [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in an ARDL framework[END_REF] and [START_REF]Asymmetric causality tests with an application[END_REF]. Furthermore, we also use the [START_REF] Kim | Unit root tests allowing for a break in the trend function at an unknown time under both the null and alternative hypotheses[END_REF] unit root test to consider potential unknown structural breaks that can exist in the time series under study. Another important point concerns the use of quarterly data instead of annual data, which have been used in most previous studies. Quarterly data allow us to increase the sample size and thus obtain more accurate empirical results.

For the case of India, 4 this study contributes to the understanding of the interplay between not only economic growth and energy consumption, as investigated in most previous studies, but also between financial development and energy consumption, which has received little attention. To the best of our knowledge, asymmetry has not been studied in either relationship. In this context, the results provided in this study can help policymakers better understand the causalities between these important economic variables and the ways in which an increase or a decrease in one variable can affect the others.

The rest of the paper is organized as follows. Section 2 reviews the literature on the two principal linkages that we study, which are the energy-growth and energy-finance nexuses. This study's contributions to these two research streams will be highlighted. Furthermore, a focus on India will also be elaborated. Section 3 presents the dataset and the methods used. The reasons that we chose to study India and its important energy and economic indicators will also be 1 Our reasons for using this variable will be explained in Section 3. 2 We would like to thank an anonymous referee for suggesting the inclusion of this variable. 3 More information on each channel is found in Section 2. 4 A focus on this country will be presented in Section 3.

analysed in Section 3. 5 Section 4 analyses the results, while Section 5 concludes the paper by paying particular attention to the policy implications of the results and the importance of taking the asymmetry into account.

Literature review: Economic activities and energy consumption

The interaction between human activities-or, more specifically, economic activities-and energy consumption is not a new topic. The first studies on this topic were published in the 1950s (e.g., [START_REF] Mason | Energy requirements and economic growth[END_REF][START_REF] Frank | Industrial capital stocks and energy consumption[END_REF]. The topic continued to be developed in later decades with particular attention on the U.S. economy (e.g., [START_REF] Schurr | Energy in the American Economy, 1850-1975[END_REF], Waren 1964[START_REF] Janosi | Patterns of energy consumption and economic growth and structure[END_REF][START_REF] Solow | The economics of resources or the resources of economics[END_REF][START_REF] Rasche | Energy resources and potential GNP[END_REF]. Only after the study of [START_REF] Kraft | On the relationship between energy and GNP[END_REF] were these studies extended to numerous countries to study various periods with various methods (e.g., Humphrey and Stainlaw (1979) for the UK; [START_REF] Zilberfarb | The energy-GDP relationship in developing economies, empirical evidence and stability tests[END_REF] for developing countries; [START_REF] Yu | The causal relationship between energy and GNP: An international comparison[END_REF] for an international comparison; [START_REF] Adams | On relative fuel efficiency and the output elasticity of energy consumption in Western Europe[END_REF] for Western Europe; [START_REF] Abakah | Real incomes and the consumption of woodfuels in Ghana. An analysis of recent trends[END_REF] for Ghana; [START_REF] Shafik | Economic growth and environmental quality: Time series and cross-country evidence[END_REF] for a panel of countries; [START_REF] Hawdon | Input-Output simulations of energy, environment, economy interactions in the UK[END_REF] for the UK; [START_REF] Masih | Energy consumption, real income and temporal causality: results from a multi-country study based on cointegration and error correction modeling techniques[END_REF] for a panel of countries; Cheng and Tai (1997) for Taiwan; [START_REF] Naqvi | A computable general equilibrium model of energy, economy and equity interactions in Pakistan[END_REF] for Pakistan). Since then, this topic has attracted an increasing number of researchers who have conducted numerous studies using data from longer periods and from a large number of countries with increasingly robust econometric methods. Different factors may explain this shift. First, energy and environmental issues have become the cornerstone of every human activity. Since the first annual United Nations meeting in Rio de Janeiro in 1992, environmental degradation has become a concern in many countries. Second, the data provided by the World Bank (World Development Indicators) have been publicly available since 2010. The existence of this online database explains the very large number of studies on this topic between 2010 and 2016, as we will demonstrate below. The relationship between financial development and energy consumption has been investigated more recently, mostly since the 2007 financial crisis (e.g., [START_REF] Claessens | Financial sector development and the millennium development goals[END_REF][START_REF] Tamazian | Does higher economic and financial development lead to environmental degradation? Evidence from BRIC countries[END_REF][START_REF] Sadorsky | The impact of financial development on energy consumption in emerging economies[END_REF]and 2011, Islam et al. 2012[START_REF] Coban | The nexus between financial development and energy consumption in the EU: a dynamic panel data analysis[END_REF][START_REF] Tang | The linkages among energy consumption, economic growth, relative price, foreign direct investment, and financial development in Malaysia[END_REF][START_REF] Komal | Linking financial development, economic growth and energy consumption in Pakistan[END_REF][START_REF] Abbasi | CO2 emissions and financial development in an emerging economy: an augmented VAR approach[END_REF].

In this context, we divide our literature review into two parts. The first part will focus on the energy-growth nexus, while the second part will focus on the linkage between financial development and energy consumption. Particular attention will be paid to the case of India.

The energy-growth nexus

The results of previous studies have not been unanimous regarding the causal relationship between energy consumption and economic growth: some researchers validate the growth hypothesis, which holds that energy consumption leads to economic growth (e.g., [START_REF] Apergis | Energy consumption and economic growth: Evidence from the commonwealth of independent states[END_REF][START_REF] Ozturk | Energy consumption and economic growth relationship: evidence from a panel data for low and middle income countries[END_REF][START_REF] Ouedraogo | Energy consumption and economic growth: evidence from the economic community of West African states (ECOWAS)[END_REF], Aslan et al. 2014b), while others validate the conservative hypothesis, which holds that economic growth influences energy consumption (e.g., [START_REF] Huang | Causal relationship between energy consumption and GDP growth revisited: a dynamic panel data approach[END_REF][START_REF] Narayan | A note on the long-run elasticities from the energy consumption-GDP relationship[END_REF][START_REF] Kasman | CO2 emissions, economic growth, energy consumption, trade and urbanization un new EU member and candidate countries: a panel data analysis[END_REF]. Some support the feedback hypothesis, which states that a bidirectional causal relationship exists between energy consumption and economic growth (e.g., [START_REF] Constantini | The causality between energy consumption and economic growth: a multi-sectoral analysis using non-stationary cointegrated panel data[END_REF][START_REF] Belke | Energy consumption and economic growth: new insights into the cointegration relationship[END_REF][START_REF] Coers | The energy-GDP nexus, addressing an old question with new methods[END_REF], while others support the neutrality hypothesis, which posits that economic growth and energy consumption are independent (e.g., [START_REF] Wolde-Rufael | Energy consumption and economic growth: the experience of African countries revisited[END_REF][START_REF] Kahsai | Income level and the energy consumption-GDP nexus: evidence from Sub-Saharan Africa[END_REF][START_REF] Smiech | Energy consumption and economic growth in the light of meeting the targets of energy policy in the EU: the bootstrap panel Granger causality approach[END_REF]. These results seem to diverge based on the countries, periods or methods used.

As explained above, a large number of studies have been published between 2010 and 2016. For example, [START_REF] Jafari | Energy consumption, economic growth and environmental pollutants in Indonesia[END_REF] found no significant relationship between energy consumption and economic growth in Indonesia from 1971 to 2007. [START_REF] Ouedraogo | Energy consumption and economic growth: evidence from the economic community of West African states (ECOWAS)[END_REF] found causality running from GDP to energy consumption in the short run and the reverse in the long run for the Economic Community of West African States. [START_REF] Dogan | Energy consumption and economic growth: Evidence from low-income countries in sub-Saharan Africa[END_REF] showed unidirectional causality running from energy use to economic growth in Kenya but no causality in Benin, Congo or Zimbabwe from 1971 to 2011. [START_REF] Nasreen | Causal relationship between trade openness, economic growth and energy consumption: A panel data analysis of Asian countries[END_REF] revealed bidirectional causality between energy consumption and economic growth in 15 Asian countries from 1980 and 2011. [START_REF] Acaravci | The electricity consumption, real income, trade openness and foreign direct investment: The empirical evidence from Turkey[END_REF] found unidirectional short-run and long-run causality running from electricity consumption to economic growth, though not the reverse, in Turkey from 1974 to 2013. Dogan (2015a) confirmed the feedback hypothesis between natural gas consumption and economic growth in Turkey from 1995 to 2012. The same result was found for Belgium's energy consumption and economic growth from 1960 to 2012 (Dogan 2016a). In Turkey from 1995 to 2013, [START_REF] Dogan | Exploring the relationship between agricultural electricity consumption and output: New evidence from Turkish regional data[END_REF] found that agricultural electricity consumption caused the output for non-coastal regions, while bidirectional causality existed between these variables for the entire panel and coastal regions. [START_REF] Fang | Energy, human capital and economic growth in Asia Pacific countries -Evidence from a panel cointegration and causality analysis[END_REF] found that economic growth caused energy use in the Asia Pacific region (16 countries) from 1970 to 2011, but the relationship may have varied for individual countries. For China, [START_REF] Furuoka | Natural gas consumption and economic development in China and Japan: An empirical examination of the Asian context[END_REF] found unidirectional causality running from natural gas consumption to economic development from 1980 to 2012. However, for Japan, the author found results that supported the feedback hypothesis. Menegaki and Tugco (2016) demonstrated the neutrality hypothesis between GDP and energy use in 42 sub-Saharan countries from 1985 to 2013.

More recently, academic research has focused on the impact of renewable energy sources on economic growth. The renewable energy sources that have been considered include hydroelectric power, geothermal power, wood, waste, biofuel, and biomass. Because investigating the linkage between renewable energy and economic growth is not the principal objective of our study, we will not develop this part further. More details can be found in recent studies, such as [START_REF] Edenhofer | On the economics of renewable energy sources[END_REF], [START_REF] Kander | Economic growth and the transition from tradition to modern energy in Sweden[END_REF], Dogan (2015b), [START_REF] Alper | The role of renewable energy consumption in economic growth: Evidence from asymmetric causality[END_REF], [START_REF] Bhattacharya | The effect of renewable energy consumption on economic growth: Evidence from top 38 countries[END_REF], [START_REF] Dogan | Analyzing the linkage between renewable and non-renewable energy consumption and economic growth by considering structural break in time-series data[END_REF], [START_REF] Marques | Renewable vs non-renewable electricity demand and the industrial production nexus: Evidence from an ARDL bounds test approach for Greece[END_REF], [START_REF] Paramati | The effect of foreign direct investment and stock market growth on clean energy use across a panel of emerging markets economies[END_REF] and [START_REF] Inglesi-Lotz | The impact of renewable energy consumption to economic growth: A panel data application[END_REF].

Of course, the above-mentioned studies do not fully represent the large body of literature on the energy-growth nexus, but this review allows us to state that the results are very different from one country to another, from one period to another and from one method to another. In our study, we will focus more on previous studies on India, which are reviewed below. [START_REF] Masih | Energy consumption, real income and temporal causality: results from a multi-country study based on cointegration and error correction modeling techniques[END_REF] found unidirectional causality running from energy to income for India's economy over the [START_REF] Mason | Energy requirements and economic growth[END_REF][START_REF] Abakah | Real incomes and the consumption of woodfuels in Ghana. An analysis of recent trends[END_REF]period. Cheng (1999) ) showed that economic growth Granger-causes energy consumption over the short term and the long term. By contrast, Asaf-Adjaye (2000) validated the conservative hypothesis. [START_REF] Ghosh | Electricity consumption and economic growth in India[END_REF] reported no long-term equilibrium between electricity consumption and economic growth but found that economic growth Granger-causes electricity consumption. [START_REF] Fatai | Modelling the causal relationship between energy consumption and GDP in New Zealand, Australia, India, Indonesia, the Philippines and Thailand[END_REF] confirmed the growth hypothesis for India within a panel of countries. Furthermore, [START_REF] Paul | Causality between energy consumption and economic growth on India: a note on conflicting results[END_REF] revealed the feedback effect between energy consumption and economic growth over the 1950-1996 period. [START_REF] Asghar | Energy-GDP relationship: a causal analysis for the five countries of South Asia[END_REF] found a neutral effect between energy consumption and GDP in India over the 1971-2003 period. [START_REF] Gupta | Causality between electricity consumption and economic growth: empirical evidence from India[END_REF] reported that electricity consumption Granger-caused economic growth over the [START_REF] Schurr | Energy in the American Economy, 1850-1975[END_REF]-2009period. Similarly, Mallick (2009) showed that, over the 1970-2005 period, economic growth fuelled demand for both crude oil and electricity, while coal consumption drove economic growth. [START_REF] Alam | Dynamic modelling of causal relationship between energy consumption, CO2 emissions and economic growth in India[END_REF] showed no causal relationship between energy consumption and income. Abbas and Choudhury (2013) validated the feedback effect (or bidirectional causality) between electricity consumption and GDP in India's agricultural sector over the 1972-2008 period. [START_REF] Ghosh | Long-term equilibrium relationship between urbanization, energy consumption and economic activity: empirical evidence from India[END_REF] exposed the unidirectional causality running from energy consumption to economic activity. [START_REF] Yang | Energy consumption, carbon emissions, and economic growth in India: evidence from directed acyclic graphs[END_REF] found that energy consumption causes economic growth. Sehrawat et al. (2015) showed a neutral effect between energy consumption and economic growth in India over the 1971-2011 period. [START_REF] Ahmad | Carbon emissions, energy consumption and economic growth: An aggregate and disaggregate analysis of the Indian economy[END_REF] found a feedback relationship between energy consumption and economic growth in India over the 1971-2014 period. [START_REF] Shahbaz | The role of globalization on the recent evolution of energy demand in India: Implications for sustainable development[END_REF] showed that economic growth and energy consumption were complementary, i.e., a feedback effect.

The energy-growth nexus in India

Overall, the existing literature on the relationship between economic growth and energy consumption in India showed divergent results (see Table 1), with annual data over various periods from the 1950s to 2014.

Conservative hypothesis

(Growth  Energy)

Feedback hypothesis (Growth ↔ Energy)

Neutrality hypothesis (Growth >< Energy) - [START_REF] Masih | Energy consumption, real income and temporal causality: results from a multi-country study based on cointegration and error correction modeling techniques[END_REF][START_REF] Mason | Energy requirements and economic growth[END_REF][START_REF] Abakah | Real incomes and the consumption of woodfuels in Ghana. An analysis of recent trends[END_REF] -Asaf-Adjaye (2000) (1973( -1995[START_REF] Fatai | Modelling the causal relationship between energy consumption and GDP in New Zealand, Australia, India, Indonesia, the Philippines and Thailand[END_REF][START_REF] Schurr | Energy in the American Economy, 1850-1975[END_REF][START_REF] Cheng | Causality between energy consumption and economic growth in India: an application of cointegration and error-correction modeling[END_REF] - [START_REF] Gupta | Causality between electricity consumption and economic growth: empirical evidence from India[END_REF][START_REF] Schurr | Energy in the American Economy, 1850-1975[END_REF]-2009) - [START_REF] Mallick | Examining the linkage between energy consumption and economic growth in India[END_REF]Mallick ( ) (1970Mallick ( -2005, for coal) - [START_REF] Ghosh | Long-term equilibrium relationship between urbanization, energy consumption and economic activity: empirical evidence from India[END_REF][START_REF] Denton | Adjustment of Monthly or Quarterly Series to Annual Totals: An Approach Based on Quadratic Minimization[END_REF]-2008) - [START_REF] Yang | Energy consumption, carbon emissions, and economic growth in India: evidence from directed acyclic graphs[END_REF]) (1970-2008) - [START_REF] Cheng | Causality between energy consumption and economic growth in India: an application of cointegration and error-correction modeling[END_REF]Cheng ( ) (1952Cheng ( -1995) ) - [START_REF] Ghosh | Electricity consumption and economic growth in India[END_REF]Ghosh ( ) (1950Ghosh ( -1997) ) - [START_REF] Mallick | Examining the linkage between energy consumption and economic growth in India[END_REF]Mallick ( ) (1970Mallick ( -2005, for oil and electricity) - [START_REF] Shahbaz | The role of globalization on the recent evolution of energy demand in India: Implications for sustainable development[END_REF]Shahbaz et al. ( ) (1971[START_REF] Shahbaz | Does financial development increase energy consumption? The role of industrialization and urbanization in Tunisia[END_REF] - [START_REF] Paul | Causality between energy consumption and economic growth on India: a note on conflicting results[END_REF]) (1950[START_REF] Sergio | Non-performing bank loans: Cyclical patterns and sectoral risk[END_REF] - Abbas and Choudhury (2013[START_REF] Janosi | Patterns of energy consumption and economic growth and structure[END_REF]-2008, agricultural sector) - [START_REF] Ahmad | Carbon emissions, energy consumption and economic growth: An aggregate and disaggregate analysis of the Indian economy[END_REF]Ahmad et al. ( ) (1971Ahmad et al. ( -2014) ) - [START_REF] Asghar | Energy-GDP relationship: a causal analysis for the five countries of South Asia[END_REF]Asghar ( ) (1971Asghar ( -2003) ) - [START_REF] Alam | Dynamic modelling of causal relationship between energy consumption, CO2 emissions and economic growth in India[END_REF]Alam et al. ( ) (1971Alam et al. ( -2006) ) - Sehrawat et al. (2015Sehrawat et al. ( ) (1971Sehrawat et al. ( -2011) ) These different results may be explained by the methods used (e.g., autoregressive distributed lag (ARDL) bounds testing, Toda and Yamamoto (2003) causality test) and the period under consideration, which vary from 1952 to 2014. The results can also be explained by the different measures of energy consumption, such as oil, electricity or coal consumption (e.g., [START_REF] Mallick | Examining the linkage between energy consumption and economic growth in India[END_REF]). We will use overall energy consumption, measured by kilos of oil equivalent per capita, to neutralize this effect. Furthermore, no study has investigated the nonlinear and asymmetric causality between economic growth and energy consumption in India, although this gap may exist because of the complexity of the causal mechanism between these variables. Following [START_REF] Chiou-Wei | Economic growth and energy consumption revisited -evidence from linear and nonlinear Granger causality[END_REF], this complexity can be explained by economic events and regime changes, such as changes in the economic environment and changes in energy policies and energy prices. In the following subsection, the same statement will be addressed in the literature regarding the linkage between financial development and energy consumption.

The nexus between financial development and energy consumption

The causality from financial development to energy consumption runs through two possible channels: the positive or the negative. With regard to the first channel, financial development increases energy consumption because it supports economic growth, which triggers a higher demand for energy (e.g., [START_REF] Sadorsky | The impact of financial development on energy consumption in emerging economies[END_REF], 2011[START_REF] Zhang | The impact of financial development on carbon emissions: an empirical analysis in China[END_REF], Aslan et al. 2014a[START_REF] Rashid | Linkage of financial development with electricity growth, nexus of India and Pakistan[END_REF]. [START_REF] Sadorsky | Financial development and energy consumption in Central and Eastearn Europe frontier economies[END_REF] explained this positive causality with three different effects: the direct effect (people buy more energy-consuming goods), the business effect (more businesses increase energy demand) and the wealth effect (higher economic confidence induces higher demand for energy). In the case of the second channel (i.e., the negative one), financial development leads to more modern and less energy-consuming technologies and, in turn, leads to a decrease in energy consumption. This channel is referred to as the technological effect (e.g., [START_REF] Tamazian | Does higher economic and financial development lead to environmental degradation? Evidence from BRIC countries[END_REF][START_REF] Jalil | The impact of growth, energy and financial development on the environment in China: a cointegration analysis[END_REF], Shahbaz et al. 2013[START_REF] Mahalik | Energy consumption, economic growth and financial development: exploring the empirical linkages for India[END_REF]. Some studies have also found bidirectional causality between financial development and energy consumption (e.g., Ali-muali andSab 2012, Shahbaz and[START_REF] Shahbaz | Does financial development increase energy consumption? The role of industrialization and urbanization in Tunisia[END_REF]. Others have found that financial development has no impact on energy consumption [START_REF] Coban | The nexus between financial development and energy consumption in the EU: a dynamic panel data analysis[END_REF]Topcu 2013, Ozturk andAcaravi 2013). Once again, the results of these studies differ based on the countries and methods used. For the methods, we have found only one study that takes into account the nonlinearity in a panel of 53 countries [START_REF] Chang | Effects of financial developments and income on energy consumption[END_REF]. However, the asymmetry has not been taken into account.

Hereafter, we will focus first on studies that analyse the impact of financial development on energy consumption in India before analysing its impact in other countries. [START_REF] Sadorsky | The impact of financial development on energy consumption in emerging economies[END_REF] found a positive and significant relationship between financial development and energy consumption when financial development is measured by stock market variables in 22 emerging countries, including India. [START_REF] Boutabba | The impact of financial development, income, energy consumption and trade on carbon emissions: evidence from the Indian economy[END_REF] validated the long-term unidirectional causality running from financial development to energy use. [START_REF] Mahalik | Energy consumption, economic growth and financial development: exploring the empirical linkages for India[END_REF] showed that financial development, economic growth and industrial development negatively affect energy consumption. [START_REF] Rashid | Linkage of financial development with electricity growth, nexus of India and Pakistan[END_REF] found that the linkage between financial development and electricity consumption via economic growth is positive and significant. By contrast, Sehrawat et al. (2015) showed a neutral effect between energy consumption and financial development in India over the 1971-2011 period. [START_REF] Shahbaz | The role of globalization on the recent evolution of energy demand in India: Implications for sustainable development[END_REF] indicated that financial development reduces energy consumption.

Financial development and energy consumption in India

Financial development and energy consumption in other countries

For Pakistan, [START_REF] Kakar | Financial development and energy consumption: empirical evidence from Pakistan[END_REF] showed that financial development affects energy consumption in the long term but not in the short term. For nine Central and Eastern European frontier economies, [START_REF] Sadorsky | Financial development and energy consumption in Central and Eastearn Europe frontier economies[END_REF] showed a positive and significant relationship between financial development and energy consumption when financial development is measured by banking variables, such as bank deposits to GDP, financial system deposits to GDP, and liquid liabilities to GDP. For Tunisia, Chtoui (2012) showed long-term bidirectional causality between economic growth and energy consumption but only unidirectional and negative causality from energy consumption to financial development. [START_REF] Shahbaz | Does financial development increase energy consumption? The role of industrialization and urbanization in Tunisia[END_REF] found that a bidirectional causal relationship exists between financial development and energy consumption and between industrialization and energy consumption in Tunisia. [START_REF] Xu | The impact of financial development on energy consumption in China: based on SYS-GMM estimation[END_REF] revealed a positive relationship between financial development and energy consumption in China when financial development is measured by the ratio of loans in financial institutions to GDP and by the ratio of foreign direct investment (FDI) to GDP. For Malaysia, [START_REF] Islam | Financial development and energy consumption nexus in Malaysia: a multivariate time series analysis[END_REF] found that energy consumption is influenced positively by economic growth and financial development in both the short term and the long term. [START_REF] Coban | The nexus between financial development and energy consumption in the EU: a dynamic panel data analysis[END_REF] indicated no significant relationship in the E27 countries. However, the relationship is positive and significant in old member countries, irrespective of whether financial development is measured using the banking sector or the stock market. 6 For Turkey, [START_REF] Ozturk | The long-run and causal analysis of energy, growth, openness and financial development on carbon emissions in Turkey[END_REF] found that financial development has a significant and positive impact on energy consumption. [START_REF] Shahbaz | The dynamic links between energy consumption, economic growth, financial development and trade in China: fresh evidence from multivariate framework analysis[END_REF] showed that, in China, financial development stimulates economic growth, which increases energy use. [START_REF] Tang | The linkages among energy consumption, economic growth, relative price, foreign direct investment, and financial development in Malaysia[END_REF] indicated that energy consumption causes financial development in Malaysia, although the feedback effect exists between energy consumption and economic growth. Altay and Topçu (2015) showed no significant relationship between financial development and energy consumption in Turkey. Using a panel of 53 countries, [START_REF] Chang | Effects of financial developments and income on energy consumption[END_REF] found that, in the non-high income regime, energy consumption increases with financial development when both private and domestic credits are used as financial development indicators. However, the results differ when financial development is measured by stock market variables (energy consumption declines when these variables increase). [START_REF] Destek | Energy consumption, economic growth, financial development and trade openness in Turkey: Maki cointegration test[END_REF] showed that, in Turkey, financial development has a negative impact on energy consumption in the long term. [START_REF] Komal | Linking financial development, economic growth and energy consumption in Pakistan[END_REF] showed that financial development positively and significantly affects energy consumption via economic growth in Pakistan. [START_REF] Shahbaz | Electricity consumption, financial development and economic growth nexus in Pakistan: a visit[END_REF] found a feedback effect, i.e., bidirectional causality between electricity consumption and financial development, in Pakistan. By contrast, some studies have shown indirect impacts of financial development on energy consumption during the global financial crisis. For China, [START_REF] Yuan | The impact on Chinese economic growth and energy consumption of the global financial crisis. An input-output analysis[END_REF] showed that the decline in exports due to the crisis led to a 7.33% decrease in GDP and a 9.21% decrease in energy consumption. [START_REF] Zhao | The impact of financial crisis on electricity demand: A case study of North China[END_REF] confirmed these results for China by considering electricity consumption, which decreased by 14.31% in 2008.

Overall, the review of the existing literature on the nexus between financial development and energy consumption shows fewer studies than those on the energy-growth nexus. The results are still unanimous regarding the causality between these two variables (see Table 2). More importantly, to the best of our knowledge, nonlinearity and asymmetry have not been taken into account. However, when studying these two aspects, considering the ambiguous relationship between financial development and energy consumption is important. [START_REF] Zhang | The impact of financial development on carbon emissions: an empirical analysis in China[END_REF] indicated that three channels explain the positive relationship between financial development and energy consumption. First, financial development attracts more FDI and thus more economic growth and energy consumption. Second, efficient financial intermediation results in more consumer loans being used to buy more energy-consuming goods, thus leading to more energy consumption. Third, stock market development helps listed firms finance more investment projects and thus increases energy consumption. By contrast, [START_REF] Chang | Effects of financial developments and income on energy consumption[END_REF] explained that financial development offers more opportunities to develop the renewable energy sector by providing more funds to innovative firms. Furthermore, FDI can lead to more technology innovations and thus help reduce energy use. This ambiguity regarding the impact of financial development on energy consumption may cause a nonlinear and asymmetric relationship between them, which is why taking these two aspects into account is important. (2015( ) (1980( -2011, Turkey, bank) , Turkey, bank) Notes: FD denotes financial development. EU denotes European Union. LR and SR denote long run and short run, respectively. The second set of parentheses show the study period and the variables used to measure financial development. "Credit" means domestic credit to the private sector, and "Stocks" means stock market variables. "Money" means money supply. "Bank" means banking variables.

Data and methodology

This section will present the dataset and the methodological framework. The reason that we have chosen to study India, as well as its important energy and economic indicators, will also be presented in the data section.7

Data

India is chosen because it is one of the largest energy consumers worldwide, behind China and the U.S. (Global Energy Statistical Yearbook 2015). According to the International Energy Agency, in 2015, India accounted for 18% of the world's population but only 6% of its primary energy usage, which shows the energy poverty in India, as analysed in [START_REF] Pachauri | On measuring energy poverty in Indian households[END_REF]. However, India's energy consumption has almost doubled since 2000, contributing to 10% of the global energy demand, while the potential for further rapid growth of GDP is great (expected to be 8% in 2017 and 2018, World Development Indicators). Furthermore, as shown in Section 2, very few studies have investigated the relationship between financial development and energy consumption in India. However, none of these studies has investigated nonlinearity or asymmetry. Other studies have been conducted on a panel of countries, including India, such as [START_REF] Tamazian | Does higher economic and financial development lead to environmental degradation? Evidence from BRIC countries[END_REF], [START_REF] Sadorsky | The impact of financial development on energy consumption in emerging economies[END_REF] and [START_REF] Talukdar | Does the private sector help or hurt the environment? Evidence from carbon dioxide pollution in developing countries[END_REF]. However, as argued in [START_REF] Boutabba | The impact of financial development, income, energy consumption and trade on carbon emissions: evidence from the Indian economy[END_REF], any potential inference drawn from these cross-country studies is widely recognized to provide only a general understanding of the linkage between the variables; thus, detailed policy implications for each country cannot be drawn from such studies [START_REF] Ang | Economic development, pollutant emissions and energy consumption in Malaysia[END_REF][START_REF] Lindmark | An EKC-pattern in historical perspective: carbon dioxide emissions, technology, fuel prices and growth in Sweden, 1870-1997[END_REF][START_REF] Stern | Economic growth and environmental degradation: the environmental Kuznets curve and sustainable development[END_REF]. As shown in Section 2, many studies have been conducted to investigate the relationship between economic growth and energy consumption in India. However, to the best of our knowledge, nonlinearity and asymmetry have not been taken into account in these studies. For all these reasons, we decided to investigate the case of India in this study.

The World Development Indicators (CD-ROM 2016) have been combed to collect data on real GDP (in constant 2010 Indian currency), energy use (kg of oil equivalent), real domestic credit to the private sector (in constant 2010 Indian currency), real gross fixed capital formation (in constant 2010 Indian currency) and labour force (measured by the proportion of the total population aged 15 to 64). We have transformed all the variables into per capita units by dividing them by the total population for each year. Real GDP per capita represents economic growth. Energy consumption is measured by energy use per capita. Real domestic credit to the private sector per capita is a measure of financial development. Real gross fixed capital formation per capita is a proxy for capital. Labour force is a proxy for the labour variable. This variable has been used in numerous studies, such as [START_REF] Soytas | Energy consumption, income and carbon emissions in the United States[END_REF], [START_REF] Soytas | Energy consumption, economic growth, and carbon emissions: Challenges faced by an EU candidate member[END_REF], [START_REF] Menyah | CO 2 emissions, nuclear energy, renewable energy and economic growth in the US[END_REF], Dogan (2015b), andStreimikienne and[START_REF] Streimikiene | Review of economic growth and energy consumption: A panel cointegration analysis for EU countries[END_REF]. To increase the accuracy of our empirical results, quarterly frequency data are used for the period from 1960Q 1 -2015Q 4 . For this purpose, we have employed the quadratic match-sum method to transform the annual frequency data into quarterly frequency data, following [START_REF] Sbia | A contribution of foreign direct investment, clean energy, trade openness, carbon emissions and economic growth to energy demand in UAE[END_REF]. 8 All the variables have been transformed into natural logarithmic form for consistent and reliable empirical results [START_REF] Shahbaz | The role of globalization on the recent evolution of energy demand in India: Implications for sustainable development[END_REF].

Numerous proxies of financial development are used by different researchers, as indicated in our literature review above. For instance, financial development can be measured by M 2 as a share of GDP or liquid liabilities as a share of GDP. The problem with these measures is that they are unable to capture the development of the financial sector. For example, M 2 is recommended by [START_REF] King | Finance and growth: Schumpeter might be right[END_REF] as a financial depth measure, but it contains a large portion of currency and thus reflects monetization rather than financial development [START_REF] Demetriades | Does financial development cause economic growth? Time series evidence from16 countries[END_REF]Hussein 1996, Jalil and[START_REF] Jalil | The impact of growth, energy and financial development on the environment in China: a cointegration analysis[END_REF]. Liquid liabilities are also used as an indicator of financial development; while they indicate the size of the financial sector, liquid liabilities are irrelevant for financial development [START_REF] Claessens | How does foreign entry affect domestic banking markets[END_REF][START_REF] Creane | Measuring financial development in the Middle East and North Africa: a new database[END_REF]. By contrast, domestic credit to the private sector refers to financial resources provided to the private sector through loans, purchases of non-equity securities, trade credit and other accounts receivable that establish a claim for repayment [START_REF] Boutabba | The impact of financial development, income, energy consumption and trade on carbon emissions: evidence from the Indian economy[END_REF]. Furthermore, domestic credit to the private sector shows the actual level of savings allocated to productive investment ventures. As such, we choose to use this variable to reflect financial development in India. Numerous previous studies, such as [START_REF] Sadorsky | Financial development and energy consumption in Central and Eastearn Europe frontier economies[END_REF], [START_REF] Mehrara | Energy consumption, financial development and economic growth: a ARDL approach for the case of Iran[END_REF], [START_REF] Polat | Revisiting linkages between financial development, trade openness and economic growth in South Africa: fresh evidence from combined cointegration test[END_REF] and [START_REF] Shahbaz | The role of globalization on the recent evolution of energy demand in India: Implications for sustainable development[END_REF], have also chosen this variable.

Figure 1 displays the time trend of the variables. More descriptions of these variables are detailed in Section 4.

Methodology

In this section, we will first detail the nonlinear and asymmetric cointegration test developed by [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in an ARDL framework[END_REF]. We will then present the nonlinear and asymmetric causality test developed by Hatemi-J (2012).

The NARDL bounds testing approach for cointegration tests

We choose to use the multivariate nonlinear ARDL (NARDL) bounds testing approach developed by [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in an ARDL framework[END_REF] because it can capture the nonlinear and asymmetric cointegration between variables. Furthermore, it distinguishes between the short-term and longterm effects of the independent variable on the dependent variable. Even if all previous facts could also be tested within a nonlinear threshold Vector Error Correction Model (VECM) or a smooth transition model, these models suffer from the convergence problem due to the proliferation of the number of parameters. However, the NARDL model does not have this problem. Moreover, unlike other error correction models where the integration order of the considered time series should be the same, the NARDL model relaxes this restriction and allows for a combination of different integration orders. This flexibility is very important, as shown in [START_REF] Hoang | Is gold a hedge against inflation? A nonlinear ARDL analysis[END_REF]. Finally, this method also helps solve the multicollinearity problem by choosing the appropriate lag order for the variables [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in an ARDL framework[END_REF]).

The NARDL model proposed by [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in an ARDL framework[END_REF] represents the asymmetric error correction model as follows:

t t q i i t q i i t q i i t q i i t q i i t q i i t q i i t q i i t i t p i t t t t t t t t t t D L L K K F F E E Y L L K K F F E E Y Y                                                                                                                 0 8 0 7 0 7 0 6 0 5 0 4 0 3 0 2 1 1 1 8 1 7 1 6 1 5 1 4 1 3 1 2 1 1 1 0 (1)
In Equation ( 1), i  denotes short-run coefficients, while i  denotes long-run coefficients with

8 ... 1  i
. We recall that a short-run analysis is intended to assess the immediate impacts of exogenous variable changes on the dependent variable. By contrast, a long-run analysis is used to measure the reaction time and speed of the adjustment towards an equilibrium level. We apply the Wald test to check the long-term asymmetry (

      
) and short-term asymmetry ( D is a dummy variable that is used to capture the impact of the structural break date (t), which is determined by the unit root test developed by [START_REF] Kim | Unit root tests allowing for a break in the trend function at an unknown time under both the null and alternative hypotheses[END_REF]. p and q represent the optimal lags for the dependent variable ( t Y ) and independent variables ( t E , t F , t K , t L ), which will be determined by the Akaike information criterion (AIC). The independent variables are decomposed into their positive and negative partial sums for increases and decreases as follows:

       ) for
  1 1 max ,0 t t t j j j j x x x             1 1 min ,0 t t t j j j j x x x           , with t x
       
. These two statistics are used in our study. If we reject the null hypothesis of no cointegration, a long-run relationship exists among the variables.

The long-term asymmetric coefficients are estimated based on

  /    mi L and   /    mi L .
These long-run coefficients, with respect to positive and negative changes of the independent variables, measure the relationship between these variables in the long-run equilibrium. For the estimation of asymmetric dynamic multiplier effects, the following equation is used:

        h j t j t h E Y m 0 ,         h j t j t h E Y m 0 ,         h j t j t h F Y m 0 ,         h j t j t h F Y m 0 ,         h j t j t h K Y m 0 ,         h j t j t h K Y m 0         h j t j t h L Y m 0 ,         h j t j t h L Y m 0 for ..... 2 , 1 , 0  h where, if   h , then    mi h L m and    mi h L m .
These dynamic multipliers show the asymmetric responses of the dependent variable to positive and negative variations in the independent variables. Based on the estimated multipliers, we can observe the dynamic adjustments from the initial equilibrium to the new equilibrium between the system variables following a variation that affects the system.

Asymmetric causality tests

To determine the direction of the asymmetric causal relationship between the variables, we employ the asymmetric causality test recently proposed by [START_REF]Asymmetric causality tests with an application[END_REF]. This test incorporates the idea behind the [START_REF] Toda | Statistical inference in vector autoregressions with possibly integrated process[END_REF] test by considering nonlinear effects and discriminates between negative and positive shocks. Hatemi-J (2012) further argued that integrated variables can be represented as a random walk process in a general form as follows:

       t i i t t t e Y e Y Y 1 1 0 1 1 and        t i i t t t e X e X
            t t i t t i t t t e e Y e Y Y 1 1 1 1 0 1 1 and             t t i t t i t t t e e X e X X 1 2 1 2 0 2 1
. In our sample, to capture the effects of both positive and negative shocks of all variables, we use the cumulative form of the following equation:

                                                  t i i t t i i t t i i t t i i t t i i t t i i t t i i t t i i t t i i t t i i t e L e L e K e K e F e F e E e E e Y e Y 1 5 1 5 1 4 1 4 1 3 1 3 1 2 1 2 1 1 1 1 , , , , , , , , , , (3)
More specifically, both positive and negative shocks indicate asymmetric causality among the variables (Hatemi-J, 2012). This test can be estimated by employing the vector autoregressive (VAR) model with an order of p . The innovative lag-based criteria suggested by [START_REF]A new method to choose optimal lag order in stable and unstable VAR models[END_REF][START_REF]Forecasting properties of a new method to choose optimal lag order in stable and unstable VAR models[END_REF]) can be used to select the optimal lag length for the VAR model.

 

p q T T n T n q A HJC j ..., , 0 , 2 ) ln(ln 2 ln ln 2 2              (4)
where j A  is a determinant in the VAR model of the obtained variance-covariance matrix of the error terms with lag order q and shows the number of equations, and T is the total number of observations in the VAR model. The null hypothesis can be represented by the kth element of . 9 This hypothesis can also be tested by employing the Wald test (Hatemi-J 2012).

  it X ,
As has been the convention in previous studies, e.g., [START_REF] Hoang | Is gold a hedge against inflation? A nonlinear ARDL analysis[END_REF], we will model our regressions based on the natural logarithms of the series under study to ensure better distributional properties.

Results and interpretations

In this section, we will first present some descriptive statistics of the variables as well as the results of the unit root tests, taking structural breaks into account. In the second subsection, we will analyse the results of the asymmetric cointegration tests (NARDL, [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in an ARDL framework[END_REF]. Finally, we will detail the asymmetric causalities between the variables based on the Hatemi-J (2012) test.

Descriptive statistics and unit root tests

Table 3 presents the results of the descriptive statistics and pair-wise correlations. We note that energy consumption is less volatile than economic growth and capital. Financial development is highly volatile compared with economic growth, energy consumption and capital. Labour has the lowest volatility, which suggests that the labour force has been stable from 1960 to 2015 in India. Furthermore, the data show an asymmetric distribution (see Skewness in Table 3).

As for the distribution of the series, we do not find a bell shape. 10 The Jarque-Bera statistics reveal the non-normal distribution of the series. These two characteristics of the series show the necessity of relying on asymmetric methods, as we do in this study. In the correlation analysis, we find a positive correlation between energy consumption and economic growth. Financial development is positively correlated with economic growth and energy consumption. A positive correlation is also found between capital and economic growth and between energy consumption and financial development. Labour force is positively correlated with economic growth, energy consumption, financial development and capital. The next step is to test the variables' stationarity properties to ensure that none is integrated at order 2 or I(2). These tests must be performed because the NARDL model of [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in an ARDL framework[END_REF] requires that the variables be integrated at orders 0 or 1 to examine the cointegration between the variables. For this purpose, we have applied the Augmented Dickey-Fuller (ADF, 1979), Phillips-Perron (PP, 1988) and Kwiatkowski et al. (KPSS, 1992) unit root tests, and Table 4 presents the results of these tests. The empirical evidence reported by the ADF unit root test shows that economic growth, energy consumption, financial development, capital and labour contain a unit root at levels associated with the intercept and trend. After being differenced once or integrated at order 1, the variables are found to be stationary. The PP and KPSS unit root tests generate similar empirical evidence. 5) Notes: ***, ** and * show significance at the 1%, 5% and 10% levels, respectively. The optimal lag length for testing the unit root properties of the variables appears in parentheses. [START_REF] Perron | The great crash, the oil price shock, and the unit root hypothesis[END_REF] noted that the unit root problem in the series may cause structural changes, and researchers should remember that traditional unit root tests may provide biased empirical results. These tests may accept a false null hypothesis by stating that a series is stationary but that a structural break may occur. This potential bias raises the issue of developing a unit root test that contains information on structural breaks in the series to achieve reliable empirical results. Furthermore, [START_REF] Kim | Unit root tests allowing for a break in the trend function at an unknown time under both the null and alternative hypotheses[END_REF] argued that traditional unit root tests provide ambiguous results due to their low explanatory power and poor size distribution, as structural breaks are treated asymmetrically not only in the null hypothesis but also in the alternative hypothesis. This issue is solved by applying the ADF unit root test that contains a single unknown structural break in the data developed by [START_REF] Kim | Unit root tests allowing for a break in the trend function at an unknown time under both the null and alternative hypotheses[END_REF]. Furthermore, this test accommodates a structural break point in the trend function with an unknown date in both the null hypothesis and alternative hypothesis. The variables are found to be non-stationary in the presence of structural breaks that occurred in 2002Q2, 2003Q2, 1998Q1, 1994Q2 and 1979Q4 for economic growth, energy consumption, financial development, capital and labour, respectively. Indeed, over the sample period, India implemented numerous economic, energy and financial policies to improve its macroeconomic performance. For example, the liberalization strategy that was adopted in the 1990s through the 2000s affected domestic production and, in turn, the overall growth of the India's economy. After the dismantling of the Administrative Pricing Mechanism (APM) on 1 April 2002, 12 India's government continued to fix the prices of liquefied petroleum gas (LPG) and kerosene oil for end users. As a consequence, the government subsidized national oil companies for the production of LPG and kerosene oil, which affected the energy demand. Furthermore, government policies to supply quality energy to the manufacturing sector also affected the energy demand in 2002. Similarly, financial reforms imposed by the International Monetary Fund (IMF) in the early 1990s also affected the financial sector's performance and increased capitalization. 13 In the first difference, economic growth, financial development, capital and energy consumption are stationary, which indicates that all the variables have the same integration order I(1). The existence of a unit root leads us to apply the NARDL bounds testing approach to examine the asymmetric cointegration among energy consumption, financial development, capital, labour and economic growth. The next subsection presents the test results.

11 We considered both the intercept and the trend while applying the ADF structural break unit test. 12 As a step towards free market pricing, the APM was dismantled in India on 1 April 2002. This step implies the removal of an administered pricing mechanism of petroleum products, such as petrol, diesel, kerosene and liquefied petroleum gas (LPG). Prior to 1 April 2002, the domestic prices of some petroleum products were partially insulated from volatile international crude oil prices (from which these products are derived), and some products were subsidized because of its wide scale usage among the oil consumers and companies in India. However, continuing with this practice was seemingly unsustainable, as the burden of energy subsidies was not only undermining India's fiscal sustainability but also reducing its energy security through investment shortfalls. These discussions also reveal that India's government has taken serious steps to deregulate energy prices from an APM regime (https://www.dnb.co.in/IndiasEnergySector2012/OilPrice.asp). 13 As such, the implementation of economic, energy and financial reforms also affected domestic output and energy consumption.

Cointegration results

The NARDL [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in an ARDL framework[END_REF] results are shown in Table 6. First, we note that financial development, energy consumption, capital and labour explain 66.31% (R 2 = 0.6631) of economic growth. Therefore, financial development, energy consumption, capital use and labour explain 66.314% of economic growth, and the error term in the model explains the rest of the variation in economic growth (33.69%). The Durbin Watson (DW) test statistic is 2.0377, indicating the absence of autocorrelation in the model estimation. This result implies that the variables considered in the production function can explain the variation in economic growth in the absence of autocorrelation. We also note the absence of serial correlation ( 2 SC  ) and White heteroscedasticity ( 2 HET  ). The functional form of the empirical model is well-designed and confirmed by the Ramsay Reset test ( 2 FF  ). This finding indicates the reliability and consistency of the empirical results. More importantly, we find that the F-statistic exceeds the upper critical bound at the 1% level of significance, which confirms the presence of cointegration among financial development, energy consumption, capital, labour and economic growth for the period of 1960Q 1 -2015Q 4 . The Wald tests show the significance of asymmetry for both the long run and short run, which implies that taking nonlinearity and asymmetry into account is important when studying the relationship between economic, financial and energy variables. The t-statistic (T BDM ) developed by [START_REF] Banerjee | Error-correction mechanism tests for cointegration in a single-equation framework[END_REF] validates the cointegration of the variables at the 1% significance level. The NARDL F-statistic (F PSS ) from [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in an ARDL framework[END_REF] confirms the existence of asymmetric cointegration among the variables, which indicates that energy consumption, financial development, capital, labour and economic growth have a long-run asymmetric association in the Indian economy. This finding confirms the importance of taking asymmetry into account when studying the relationship among these variables. More detailed results are presented below.

In the long term

A positive shock in financial development has a negative and significant effect on economic growth (a significant coefficient of -0.0289), which indicates that any positive shock to financial development hampers economic growth in India. During the development of the Indian banking sector in the 1990s, 14 India followed an easy monetary policy to enhance credit allocation to the private sector at lower costs. This policy enabled people (e.g., households and businesses) to access financial resources to mitigate their consumption and investment activities. In this context, the negative coefficient indicated above thus suggests that credit allocated to people by the banking sector was not invested properly. People may have carelessly consumed and invested borrowed money for unproductive consumption and investment activities. This inefficiency in credit use diminishes the potential growth of the Indian economy. By contrast, a negative shock in financial development is positively linked with economic growth (a coefficient of 0.0721). This finding implies that any negative shock to financial development plays a stimulating role in India's long-term economic growth. If an easy monetary policy (promoted by the Central Bank, for example) leads to an inflationary situation, then commercial banks are asked to increase the interest rate (costs of borrowing) on both consumer and investment loans. At the same time, commercial banks are also asked to increase the interest rate on banking deposits to maintain price stability. Such conventional tight monetary policy reduces the level of credit granted to various economic sectors (e.g., households and business firms). This explanation further reveals that a negative shock to the development of the banking sector can create a predictable rationality in the minds of consumers and investors, whereby further credit expansion will be limited. Hence, they tend to become much more careful about the future productive use of loans granted by the banking sector. As a result, the economy's real production and growth are enhanced.

In the long term, energy consumption (positive shock) always positively affects economic growth (with a coefficient of 0.1038), indicating that any positive shock to energy consumption plays an enabling role in stimulating growth and development in the Indian economy. Both economic growth and energy demand are highly correlated in the long term, which explains this finding. We also find that a negative shock in energy consumption has a negative effect on economic growth (with a coefficient of -0.5788). This negative coefficient indicates that any negative shock to energy consumption will hamper economic growth in India. For instance, if the Energy Conservation Act 15 (ECA 2001) by India's government took effect due to unpredictable climate change and global warming, various sectors would be directed to reduce excessive 14 India's banking sector was initially composed of 28 public sector banks-the majority government-owned-23 private banks and 27 foreign banks. Private sector banks were barred from the banking market after the nationalization of 14 private commercial banks on 19 July 1969. In 1980, 6 additional commercial banks were nationalized. After this period, the public sector expanded their banking activities, and the nationalization of private commercial banks created a new chapter in Indian banking history, primarily for two reasons. First, the central government needed financial help in monetizing the fiscal deficit and thus took ownership of these banks. Second, the central government took ownership of 20 private commercial banks between 1969 and 1980 to increase the confidence level of the people. Other important changes were implemented after 1990. First, the market was opened up to private sector banks and foreign banks. Second, the regulations that governed the establishment of branches were amended. Third, regulations relating to lending were eased. Fourth, public sector banks were allowed to procure financial resources from the stock market up to 49% of their paid capital. The state of affairs began to change after 2000. The government adopted a policy of converting financial institutions into banks, and the Industrial Credit and Investment Corporation of India (ICICI) became a bank in 2001, followed by Industrial Development Bank of India (IDBI) in 2004. During this period, one public sector bank and four private sector banks were established, and 16 foreign banks entered the market. In March 1991, foreign banks had 151 branches in India, increasing to 205 branches by March 2001 and to 295 branches by March 2009 [START_REF] Khan | Growth and development in Indian banking sector[END_REF]. 15 The main objective of the ECA ( 2001) is to reduce the long-run demand for energy consumption and to achieve sustainable economic development. Moreover, the novelty of this act shows that India is not worse off by reducing the intensity of energy consumption, which is required for the production and consumption of goods and services; the Indian economy has been able to maintain a high growth momentum over the years. energy use. This negative shock to energy demand would eventually reduce the expansion of various economic activities and thereby lower economic output and growth in the long term. Drawing on our understanding of these discussions, we urge the Indian government and policymakers to be very careful and sensible in harnessing the effective use of energy in various productive sectors of the economy through proper energy efficiency channels. Doing so allows for the reduction of energy consumption while maintaining economic growth.

In the long term, positive shocks in capital always have positive effects on economic growth (a coefficient of 0.0666). Any positive shock to capital warrants greater long-term fiscal investments in infrastructure development and thereby increases economic growth in the long term. From a policy perspective, both policymakers and governments in emerging countries, such as India, should always monitor productive capital investments when designing public policy that aims to achieve sustainable long-term economic growth and development. By contrast, negative shocks in capital also have a positive effect on economic growth (a coefficient of 0.0737). However, this coefficient is significant only at the 10% level, indicating that capital formation is the ultimate engine of economic growth in India. 16 This finding is also consistent with the results of [START_REF] Sahoo | Infrastructure development and economic growth in India[END_REF] for India and [START_REF] Sahoo | Infrastructure development and economic growth in China[END_REF] for China, who find a positive effect of capital formation on economic growth. Although the positive effect of capital on economic development has been well documented in the literature, surprisingly, the positive effect of a negative shock in capital formation on economic growth is found in India. This may be possible in the sense that the distinctive developmental feature of the Indian economy is accepted with added characteristics from both the agriculture sector and the industrial sector. The agriculture sector in India mostly operates with a capital shortage (e.g., a negative shock in capital formation), coupled with labour abundance that not only produces disguised labour unemployment but also creates productivity losses in existing surplus labour. Increasing labour unemployment and productivity losses are the products of the capital shortage in the agriculture sector, which may harm economic growth in the long run. However, the harmful effect of a negative shock to capital formation on economic growth may not occur in the Indian case mainly due to the better connectivity between the agriculture and industrial sectors. The industrial sector normally absorbs excess labour from the agriculture sector for its own production purposes. As a result, increasing labour employment in the industrial sector will not only increase the efficiency level of the agriculture sector but also increase economic growth in India.

Finally, the relationship between a positive shock in the labour force and economic growth is positive and significant (a coefficient of 1.3996). However, a negative shock in the labour force does not have a significant impact on economic growth. The positive impact of an increased labour force on economic growth shows that any policy in India that aims to increase the level of labour employment will not only stimulate consumption and investment activities but also contribute to economic growth. As such, policymakers in India should consider the labour force an important determinant of economic growth, mainly in the augmented production function. By contrast, a negative shock in the labour force has an insignificant effect on economic growth in India. Increased life expectancy and a lower fertility rate due to an ageing population are accepted as the most important demographic factors that reduce the number of young workingage people participating in the total labour force, which explains this insignificant effect [START_REF] Yenilmez | Economic and social consequences of population aging the Dilemmas and opportunities in the Twenty-First Century[END_REF]. This reduced participation further increases the proportion of the older people in the labour force and thereby limits the growth of the total labour supply in the economy. Such a negative shock in the labour force does not have any effect on economic growth, which may be due to the increasing retirement age in India. Moreover, increasing the retirement age is only one factor that resolves the labour supply shortage, along with maintaining a similar trend of Indian economic growth in the short run.17 Our finding is not consistent with the recent results of [START_REF] Ismail | Does population aging affect economic growth in Malaysia?[END_REF] for the Malaysian economy, where they have found that a declining fertility rate contributes to higher economic growth.18 However, our finding predicts that Indian economic growth may not be stable in the long run mainly due to the ageing population. We argue that, to have a stable 'growth miracle' for the Indian economy and a constant age structure over time, policymakers in India must consider taking advantage of demographic dividend associated with increasing the employment of the young working-age population in the total labour force. India represents an emerging economy with a relative abundance of labour, where the population is assumed to be very young; as such, the Indian government should recognize the young population as a gift or a demographic dividend by providing them with an essential skill-based education in the long run.

In the short term

A negative shock in financial development is positively related to economic growth (a coefficient of 0.3148), which shows that, due to increasing borrowing costs, the limiting credit multiplier practice in the banking sector will not reduce economic growth in India. Indeed, the number of loans granted by the banking sector at higher interest rates will serve as a worthy lesson to various types of borrowers; they will learn to use borrowed money more efficiently, especially in the short term. This understanding becomes important for borrowers because, unless they use money in a productive manner, they will fail to repay the principal amounts and higher interests. If such failures occur, the borrowers' lack of creditworthiness will be highly visible to financial institutions, which, in turn, will deny them further credit. As such, we also believe that granted loans will be effectively used by borrowers for productive purposes and will thus contribute to greater economic growth in the short term.

In addition, we find that energy consumption (a negative shock) is negatively linked to economic growth in the short term (a coefficient of -1.1696). An adverse effect of a negative shock in energy consumption on economic growth is possible in respecting any energy conservation act proposed by any government. If producers make any attempt to reduce energy usage in production activities, economic growth will be diminished in the short term in India. For capital use, both positive and negative shocks have a positive impact on economic growth in the very short term (at lag 0, coefficients of 0.2168 and 0.3, respectively). However, a positive shock in capital in the previous periods (lags 1 and 2) is negatively linked with economic growth (coefficients of -0.0991 and -0.0965, respectively). A negative shock (at lag 1) has an inverse relationship with economic growth (coefficient of -0.1138). These findings highlight the important role of capital use in short-term economic growth, as any positive shock to capital formation in the previous period will have an adverse effect on economic growth in the following period. These findings further imply that, if the Indian government gives greater investment priority to infrastructure development in the previous period, the economic output in the present period will not be greatly affected. Positive shocks in labour increase economic growth in the same period (coefficient of 8.448 at lag 0), but positive shocks in the labour force in the previous period significantly decrease economic growth in the present period (coefficient of -5.226 at lag 1). For instance, although the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA, 2005) has been implemented in India, the rural poor have been unable to benefit from this scheme, which may be due to the lack of effective implementation at a district level. Hence, the positive shock to the labour force will increase unemployment and thus undermine economic growth. On policy grounds, we suggest that fiscal governments in India need to check the effective implementation of the Employment Guarantee to increase long-run economic growth. Last, a dummy variable (2002Q II ) has a positive and significant impact on economic growth (a coefficient of 0.0081), which implies that the liberalization adopted in the 1990s and 2000s has proved to be a fruitful tool for enhancing domestic production and, in turn, economic growth (see Section 4.1. for more details).

Finally, we applied multiple dynamic adjustments (more details in Section 3.2.1). The results are shown in Figure 2, which plots the cumulative dynamic multipliers. These multipliers show the pattern of adjustment of economic growth in its new long-term equilibrium following a negative or positive unitary shock in energy consumption, financial development, capital and labour, respectively. The dynamic multipliers are estimated based on the best-fitted NARDL model chosen by the AIC. The positive (continuous black line) and negative (dashed black line) curves capture the adjustment of economic growth to positive and negative shocks of the abovementioned variables at a given forecast horizon, respectively. The asymmetric curve (continuous red line) reflects the difference between the dynamic multipliers associated with positive and negative shocks, i.e.,

   h h m m
. This curve is displayed with its lower and upper bands (dotted red lines) at a 95% confidence interval to provide a measure of the statistical significance of asymmetry at any horizon h.

The graphs in Figure 2 confirm the existence of an overall positive relationship between energy consumption and economic growth. The effect of a positive shock in energy consumption is found to dominate that of a negative shock with an initial positive effect. Moreover, a significant asymmetric response to shocks in energy consumption is observed. Similarly, we note the positive association between financial development and economic growth. This result indicates that a positive shock in financial development dominates a negative shock in financial development. Thus, the role of a positive shock in financial development exceeds that of a negative shock in financial development on economic growth in the long term. This finding confirms that the long-term coefficient of a positive shock in financial development is higher than that of a negative shock in financial development (0.0721 vs -0.0289, see Table 6). Furthermore, an overall positive association exists between capital and economic growth because negative shocks in capital have dominating positive effects on economic growth. However, a positive shock in labour dominates its negative shock. This finding confirms the previous result, in which a negative shock in labour has an insignificant impact on economic growth.

Asymmetric causalities between energy consumption, financial development and economic growth in India

As explained in Section 3.2.2, we also applied asymmetric and non-asymmetric causality tests of Hatemi-J (2012) to examine the causality between energy consumption, financial development, capital, labour and economic growth in India. The results are reported in Table 7. indicates no causality / unidirectional causality, respectively. CV denotes the critical value. The information criterion used for lag selection is the Hatemi-J Criterion (HJC). An extra unrestricted lag was included in the VAR model to account for the effect of a unit root, as suggested by [START_REF] Toda | Statistical inference in vector autoregressions with possibly integrated process[END_REF]. * , ** and *** show significance at the 1%, 5% and 10% levels, respectively.

From Table 7, a neutral effect is noted between positive shocks in energy consumption and economic growth (line 5), but a negative shock in energy consumption is found to cause a negative shock in economic growth (line 6). The symmetric causality between economic growth and energy consumption is unidirectional (line 4), running from energy consumption to economic growth. This empirical evidence is consistent with [START_REF] Paul | Causality between energy consumption and economic growth on India: a note on conflicting results[END_REF], Wolde-Rufael (2010), 19 [START_REF] Alam | Dynamic modelling of causal relationship between energy consumption, CO2 emissions and economic growth in India[END_REF] and [START_REF] Mahalik | Energy consumption, economic growth and financial development: exploring the empirical linkages for India[END_REF]. However, when we observe the asymmetric results, they are only true for a negative shock in energy consumption (line 6). Previous studies have not addressed this result because the asymmetric effect has not been investigated. However, this finding partly supports the findings of [START_REF] Mallick | Examining the linkage between energy consumption and economic growth in India[END_REF], [START_REF] Mandal | Causality between energy consumption and output growth in the Indian cement industry: An application of the panel vector error correction model (VECM)[END_REF][START_REF] Mandal | Causality between energy consumption and output growth in the Indian cement industry: An application of the panel vector error correction model (VECM)[END_REF][START_REF] Shahbaz | The role of globalization on the recent evolution of energy demand in India: Implications for sustainable development[END_REF], who report a feedback effect between energy consumption and economic growth. Energy conservation policies are thus suggested to impede economic growth, which, in turn, reduces energy demand (positive causality from negative shocks in energy consumption to negative shocks in economic growth, line 6). This finding again underlines the importance of considering the asymmetric causality between energy consumption and economic growth.

The asymmetric causal relationship between positive shocks in financial development and economic growth is not significant (line 10). This finding reveals that the relationship between domestic financial development and economic growth appears to be neutral, as increasing economic output and growth do not necessarily depend on positive shocks to financial development, and vice versa. Any fiscal and monetary efforts to expand financial services to people will not increase economic output and growth. However, the unidirectional causality is confirmed to run from a negative shock in financial development to a negative shock in economic growth (line 11). A negative shock in financial development will thus hamper economic growth in India. For example, following the increasing prices of goods and services, monetary policy is forced to use a contractionary policy mechanism by increasing the costs of borrowing (interest rates) to maintain price stability. An increasing interest rate on borrowed amounts becomes expensive for people seeking credit from financial institutions. This expense leads to a decrease in credit demand, thereby reducing economic output and growth. Therefore, negative shocks in financial development may have negative impacts on economic growth. The symmetric causality analysis reports that financial development causes economic growth (line 12), a supply-side effect; in return, economic growth causes financial development (line 9), a demand-side effect. This finding contrasts with [START_REF] Chakraborty | Financial development and economic growth in India: an analysis of the post-reform period[END_REF], who reported that stock market development plays a minor role in stimulating the domestic economy. Similarly, [START_REF] Pratap | Nexus of financial development and economic growth in India: revisiting Schumpeter[END_REF] reported that financial development plays an insignificant role in economic growth. However, the empirical findings of Sehrawat and Giri (2015) validated the presence of a supply-side effect for the Indian economy.

By contrast, we find no significant causality between financial development and energy consumption,20 which indicates that increasing financial development will not increase energy demand, and vice versa. In this case, adding financial development to the energy demand function will not help policymakers in India reduce the target level of energy use for the sake of better environmental quality. Policymakers should instead promote financial development by adding potential variables other than energy consumption, as it has no effect on financial development. This result confirms that of Sehrawat et al. (2015) for India, Coban and Topçu (2013) for European countries, that of Shahbaz et al. (2013a) for China and that of Aytay and Topçu (2015) for Turkey.

A neutral effect is found between positive shocks in capital and economic growth (line 16). A similar result is also found for negative shocks in capital and economic growth (line 17). In the symmetric causality results, we find that economic growth does not Granger-cause capital; instead, capital Granger-causes economic growth (lines 18 and 19). This finding implies that economic growth is not possible in the absence of capital formation but that capital formation is possible in the absence of economic growth. From a policy perspective, we suggest that any fiscal policy designed to reduce gross fixed capital formation will hamper economic growth. Similarly, Mehta (2009) indicated that capital formation plays a vital role in enhancing the domestic economy and thus promotes economic growth.

Finally, we note the neutral causal effect (both in symmetric and asymmetric analyses) between the labour force and economic growth in India (lines 20 to 24). This finding further suggests that the Indian economy can grow and prosper without a substantial contribution from the labour force in the short run. Alternatively, it confirms that the growing pace of the Indian economy may be intensified with continued support from capital formation in the exogenous production function, where greater technological progress plays a vital role rather than the labour force. Above all, this noteworthy finding is consistent with the recent empirical results of [START_REF] Lahoti | Economic growth and female labor participation in India[END_REF], who also find a non-significant long-run relationship between the female labour participation rate and economic growth in the Indian case. This may be possible thanks to the stability of the labour force in India within the study period, as shown by its low standard deviation in Table 3. On policy grounds, these results suggest that policymakers in India should pay less attention to the role of the labour force when augmenting the production function. Such policy further justifies India's reduced spending attitude towards labour market development in the short run.

Conclusion and Policy Implications

This paper applied a neo-classical augmented production function in examining the relationship between energy consumption and economic growth by incorporating financial development, capital and labour into the Indian economy. The study used quarterly frequency data for the 1960Q1-2015Q4 period. Furthermore, we employed the nonlinear and asymmetric ARDL cointegration approach developed by [START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in an ARDL framework[END_REF]. The causal relationship between the variables was investigated with the asymmetric Granger causality test developed by Hatemi-J (2012). The empirical results provide strong support for the presence of an asymmetric cointegration association between the variables under study. The causality results also show the importance of considering asymmetry, as we will demonstrate below.

If we only relied on symmetric analysis, we would be unable to conclude that only negative shocks in energy consumption have negative impacts on economic growth. By contrast, positive shocks in energy consumption do not have a substantial impact on economic growth. This finding is important because it suggests that any policy that aims to reduce energy consumption in India will impede economic growth but that an increase in energy consumption will not be the solution to improve economic growth in India. In this case, the Indian government should maintain stable energy consumption but not reduce it. To do so, the government can make efforts to encourage the use of more energy-saving technologies in various sectors' production and consumption activities and the efficient harnessing of given energies through the proper channels.

As for the interaction between financial development and economic growth, the results show that taking asymmetry into account is important. Indeed, a positive shock in financial development does not affect economic growth, but a negative shock in financial development hampers domestic economic output. For instance, the recent Financial Stability Report (2016) of the Reserve Bank of India (RBI) reveals increasing non-performing assets (NPAs) or bad loans mainly in the Indian public sector and scheduled commercial banks. This report shows that both the public sector and scheduled commercial banks have imprudently lent large sums of money to large corporations21 without evaluating their creditworthiness [START_REF] Ranjan | Non-performing loans and terms of credit of public sector banks in India: An empirical assessment[END_REF]. 22Eventually, these banks reported losses due to the payment failures of large corporations in India. The greater losses in these banks not only harm their balance sheets due to unrealistic lending practices in relation to large corporations but also adversely affect Indian macroeconomic performance as a whole. In such circumstances, we hint that the monetary policy committee (MPC) in India should direct both the public sector and scheduled commercials banks to advance their credit allocation only towards productive sectors by tightening credit provisioning rules for the bad loans that they generated in the economy. If such a policy continues in right direction, then the credit expansion of these banks in an ethical monetary policy framework will be unlikely to undermine India's economic growth in the long run. From a policy perspective, this result further suggests that policymakers in India should carefully check the proper allocation of financial resources across various competing consumption and business activities to minimize the negative consequences of growth, such as increasingly stressed assets in the public sector and scheduled commercial banks.

However, regarding capital formation, we find symmetric unidirectional causality running from capital to economic growth, which implies that capital is vital for achieving long-run economic growth. For sustainable economic growth, we suggest that fiscal governments in India invest more money in establishing better infrastructure development. In addition, policymakers should augment the production function by adding capital as a growth-enhancing determinant in the Indian economy. If collaboration and better understanding between fiscal governments and policymakers in India continue to fail, then the Indian economy will be more likely to grow and prosper, but the level of development will not be sustainable. As a consequence, retarding "people and energy poverty" will remain a serious challenge for the Indian economy in the future.

Finally, we find no significant causal relationship between the labour force and economic growth in India over the study period, which shows that economic growth is possible without heavy investments in the labour force. The simple reason for this finding is that, in the age of technology, producing agricultural output is becoming much easier. With rapid economic growth and advancing urbanization, the labour force is migrating to urban areas in search of better job opportunities. As a result, farmers in the Indian agriculture sector also experience the labour shortage and, in turn, tend to utilize new technologies for better production. As such, agricultural output is expected to increase with the help of technology. Finally, we suggest that policymakers in India protect the rapid and environmentally friendly growth of the Indian economy by adding 'energy-saving' and 'growth-stimulating' technologies to the production function.

Figure

  Figure 1: Trends in GDP per capita, energy use per capita, financial development, capital and labour force in India from 1960 to 2015

Figure 2 :

 2 Figure 2: Dynamic multiple adjustments of economic growth to a unitary variation of energy consumption, financial development, capital and labour

Table 1 : The linkage between economic growth and energy consumption in India Growth hypothesis

 1

(Energy  Growth)

Table 2 : The nexus between financial development and energy consumption

 2

	Positive effect	Negative effect	Conservative effect	Feedback effect	Neutrality effect
	(FD  + Energy)	(FD  -Energy)	(Energy  FD)	(FD ↔ Energy)	(FD >< Energy)
	India	India	Other countries	India	India
	-Sadorsky (2010)	-Mahalik and	-Chtoui (2012)	-Shahbaz et al.	-Sehrawat et al.
	(1999-2006,	Mallick (2014)	(1972-2010, Tunisia,	(2016) (1971-2012,	(2015) (1971-2011,
	stocks)	(1971-2009,	credit, SR)	credit)	credit)
	-Boutabba (2014)	credit)	-Tang and Tan
	(1971-2008, credit)	-Shahbaz et al.	(2014) (1972-2009,	Other countries	Other countries
	-Rashid and	(2016) (1971-	Malaysia, bank)	-Kakar et al. (2011)	-Coban and Topçu
	Yousaf (2015)	2012, credit)		(1980-2012,	(2013) (1999-2011,
	(1980-2011, credit)			Pakistan, credit)	new EU members,
				-Shahbaz and Lean	credit, bank)
	Other countries	Other countries		(2012) (1971-2008,	-Shahbaz et al.
	-Kakar et al.	-Chtoui (2012)		Tunisia, credit, LR)	(2013a) (1971-2011,
	(2011) (1980-	(1972-2010,		-Shahbaz (2015)	China, credit)
	2012, Pakistan,	Tunisia, credit,		(1972-2012,	-Altay and Topçu
	money)	LR)		Pakistan, credit)
	-Sadorsky (2011)	-Chang (2015)
	(1996-2006, EU,	(1999-2008, 53
	bank, stocks)	countries, stocks)
	-Xu (2012) (1999-	-Destek (2015)
	2009, China, credit,
	FDI)
	-Islam et al. (2013)
	(1971-2009,
	Malaysia, credit)
	-Coban and Topçu
	(2013) (1999-
	2011, old EU
	members, credit,
	bank)
	-Ozturk and
	Acaravi (2013)
	(1960-2007,
	Turkey, credit, SR)
	-Chang (2015)
	(1999-2008, 53
	countries, bank)
	-Komal and Abbas
	(2015) (1972-
	2012, Pakistan,
	credit)

1: Trends in GDP per capita, energy use per capita, financial development, capital and labour force in India from 1960 to 2015

	90,000						700
	80,000
							600
	70,000
	60,000						500
	50,000
	40,000						400
	30,000
							300
	20,000
	10,000						200
	1960	1970	1980	1990	2000	2010	1960	1970	1980	1990	2000	2010
	c). Financial development (in real 2010 Indian currency)	d). Capital (in real 2010 Indian currency)
	50,000						30,000
	40,000						25,000
							20,000
	30,000
							15,000
	20,000
							10,000
	10,000						5,000
	0						0
	1960	1970	1980	1990	2000	2010	1960	1970	1980	1990	2000	2010
	e). Labour (% of population aged 15 to 64)
	66
	64
	62
	60
	58
	56
	54
	1960	1970	1980	1990	2000	2010

a). GDP per capita (in real 2010 Indian currency) b). Energy use per capita (kg of oil equivalent)

  To test the existence of an asymmetric long-run cointegration,[START_REF] Shin | Modelling asymmetric cointegration and dynamic multipliers in an ARDL framework[END_REF] propose the bounds test, which is a joint test of all the lagged levels of the regressors. Two tests are used: the t-statistic of[START_REF] Banerjee | Error-correction mechanism tests for cointegration in a single-equation framework[END_REF] and the F-statistic of[START_REF] Pesaran | Bounds testing approaches to the analysis of level relationships[END_REF].

	representing t E , t F , t K and t L .
									The t-statistic tests
	the null hypothesis			0	against the alternative hypothesis			0	. The F-statistic tests the null
	hypothesis of				0

  X

																			1	2	0	2	(2)
																				1
	where	t 	, .... , 2 , 1	T	,	0 Y and 0 X denote initial values, and t e 1 and	t e 2 are the error terms.
	1 i e  	max( 1 i e	,	0	)	and	2 e  i 	max( 2 e	i	,	0	)	represent positive shocks, and	1 i e  	min( 1 i e	,	0	)	and
	2 e  i	min( 2 e	i	,	0	)

 represent negative shocks. In an asymmetric framework, it is also represented by

  which does not affect the

						wth	t Y		because it is defined as	: H row , column k element 0
	in r A equals zero for	r		, 1	....,	p

Table 3 : Descriptive statistics and pair-wise correlations Variable

 3

		t Y	E	t	t F	K	t	t L
	Mean	32385.06	363.1979	10533.27	8067.963	58.8983
	Median	24134.27	330.9751	6007.104	5301.509	57.7425
	Maximum	86579.14	639.4249	45583.94	25397.04	65.5957
	Minimum	15203.85	232.0468	1171.907	1940.468	55.2194
	Std. Dev.	19237.91	112.791	12011.13	7094.954	3.1069

Table 4 : Unit root analysis without structural break

 4

					ADF		PP	KPSS
	Variables	Level	1 st Diff.	Level	1 st Diff.	Level	1 st Diff.
	t Y	3.1851 (9)	-3.0357 (8) **	6.3287 (0)	-7.6775 (17) ***	1.8564 (11) ***	1.3818 (6)
	E	t	1.1253 (9)	-3.2709 (8) **	1.4551 (7)	-7.3955 (19) ***	1.8957 (11) ***	0.3514 (7)
	t F	2.1204 (9)	-2.3291 (8) *	5.3989 (2)	-8.4590 (22) ***	1.8855 (11) ***	1.4283 (7)
	K	t	0.9300 (13)	-3.1927 (12) **	0.9953 (5)	-7.4546 (20) ***	1.9097 (11) ***	0.2692 (5)
	L	t	-2.0495 (5)	-3.9202 (3) **	-2.4578 (3)	-3.6657 (3) **	2.1019 (9) ***	0.9876 (

Table 5 : Unit root analysis with an unknown structural break Variable ADF Test at Level ADF Test at 1st diff. Statistics Break Date Statistics Break Date

 5 Table 5 presents the test results. 11

	t Y	1.0630	2002Q2	-4.6012 ***	1993Q1
	E	t	-0.1100	2003Q2	-4.4788 ***	2003Q1
	t F	-1.6082	1998Q1	-4.2886 **	1998Q1
	K	t	-1.0669	1994Q2	-4.2192 **	2011Q1
	L	t	-2.1487	1979Q4	-4.5071 **	1966Q1
	Significance
	CV 1%	-4.949133
	CV 5%	-4.443649
	CV 10%	-4.193627
	Notes: *** and ** show the significance at the 5% and 10% levels,
	respectively.

Table 6 : Cointegration results

 6 LR W and SR W represent the Wald test for the null of longterm and short-term symmetry for the respective variables. F PSS shows the statistics from the Pesaran et al. (2001) bounds test. T BDM shows the statistics from Banerjee et al. (1998). The p-values appear in brackets. ** and *** indicate significance at the 5% and 1% levels, respectively.

	Dependent Variable: t Y
	Variable	Coefficient	T-Statistic	Prob.
	Constant	1.7063 ***	7.5549	0.0000
	Y	t	1 		-0.2070 ***	-7.5469	0.0000
	t F	 1		-0.0289 ***	-5.1273	0.0000
	t F	 1		0.0721 **	2.0828	0.0385
	E	 1 t	0.1038 ***	4.6360	0.0000
	E	 1 t	-0.5788 **	-3.2785	0.0012
	K	 1 t	0.0666 ***	5.5533	0.0000
	K	 1 t	0.0737 *	1.8286	0.0689
	 1 t L	1.3996 ***	5.2177	0.0000
	 1 t L	-0.1214	-0.6469	0.5184
	QII D  2002	0.0081 **	2.4800	0.0141
	 t Y	1 	0.4616 ***	7.7617	0.0000
	 t Y		2	0.2155	3.5486	0.0005
	 t F		0.3148 ***	4.3628	0.0000
	  t E	-1.1696 ***	-2.6400	0.0089
	  t K	0.2168 ***	5.3810	0.0000
	  t K	0.3000 ***	4.8718	0.0005
	   1 t K	-0.0991 **	-2.2000	0.0275
	   2 t K	-0.0965 **	-2.2866	0.0223
	   1 t K	-0.1138 ***	-1.8571	0.0648
	  t L	8.4480 ***	3.0391	0.0027
	   1 t L	-5.2268 *	-1.8899	0.0602
	R 2		0.6631
	Adj-R 2	0.6293
	D-W Test	2.0377
			2 SC	1.3611	[0.2587]
			2 HET	1.2780	[0.2322]
			2 FF	1.4020	[0.1090]
	 F L		-0.1400 ***	 F L	0.3482 **
	 E L		0.5016 ***	 E L	2.7961 ***
	 K L		0.3218 ***	 K L	0.3560 ***
	L	 L		6.7614 ***	L	 L	-0.5867
	F W , LR	7.3811 ***	F W , SR	13.2161 ***
	E W , LR	8.0114 ***	E W , SR	16.9004 ***
	K W , LR	9.0610 ***	K W , SR	19.1180 ***

Table 7 : The asymmetric and non-asymmetric causality analysis

 7

				Causalities	Test value	CV at 1%	CV at 5%	CV at 10%
	t Y 	E	t	(1)	8.578	25.928	20.525	17.860
	Y	t	   t E	(2)	0.053	13.874	7.712	5.040
	Y	t	   t E	(3)	10.470	25.932	16.114	11.633
	t E  (4) t Y	32.510 ***	28.791	22.324	19.152
	E	  t t Y			(5)	0.0790	11.119	7.158 0	5.0140
	E	  t t Y		(6)	77.538 ***	51.963	17.024	10.875
	Y	t	  t F			(7)	1.637	10.112	6.547	4.768
	Y	t	  t F			(8)	0.783	21.096	7.765	4.515
	t F Y  (9) t	30.994 ***	28.472	22.248	18.982
	t F	  t Y			(10)	0.602	10.149	6.399	4.877
	t F	  t Y		(11)	12.483 **	25.743	9.004	4.622
	t F  (12) t Y	12.483 **	25.743	9.004	4.622
	t Y 	K	t	(13)	16.690	28.305	21.146	18.144
	Y	t	   t K		(14)	1.557	9.743	6.940	5.278
	Y	t		   t K		(15)	1.245	15.138	7.769	4.517

We would like to thank an anonymous referee for suggesting this organization.

For new members, however, only banking variables have a significant relationship with energy consumption. The old members are Luxembourg, the UK, the Netherlands, Spain, Portugal, Germany, Austria, Belgium, Ireland, Denmark, France, Finland, Greece, and Sweden. The new members are Cyprus, Malta, the Czech Republic, Slovakia, Estonia, Hungary, Bulgaria, Slovenia, Poland, Latvia, Lithuania, and Romania.

We would like to thank an anonymous reviewer for this suggestion.

A mechanism of data transformation from a low frequency into a higher frequency is said to enable the quadratic match-sum method that is used to adjust seasonal variations in the raw data used in the analysis. Similarly,[START_REF] Cheng | Tracking the Hong Kong Economy[END_REF] also noted that the quadratic match-sum method easily captures the point-to-point variations in the data to address the seasonality problem. Hence, we prefer the quadratic match-sum method, as it transforms annual data into quarterly data following[START_REF] Denton | Adjustment of Monthly or Quarterly Series to Annual Totals: An Approach Based on Quadratic Minimization[END_REF].

More details can be found in Hatemi-J (2012).

Detailed results are available upon request.

The role of capital formation in economic growth and development has been well established in the field of macroeconomics[START_REF] Aschauer | Is public expenditure productive[END_REF] World Bank, 1994;[START_REF] Sahoo | Economic growth in South Asia: Role of infrastructure[END_REF][START_REF] Sahoo | Infrastructure development and economic growth in China[END_REF].

See a recent study by[START_REF] Yenilmez | Economic and social consequences of population aging the Dilemmas and opportunities in the Twenty-First Century[END_REF] for the economic consequences of population ageing in the 21 st century.

Our finding is also consistent with the results of[START_REF] Lahoti | Economic growth and female labor participation in India[END_REF], who fail to find a significant long-run relationship between female labour force participation and economic development in India.

[START_REF] Wolde-Rufael | Bounds test approach to cointegration and causality between nuclear energy consumption and economic growth in India[END_REF] used nuclear energy consumption as a measure of energy use.

The results regarding the causality between the independent variables are not shown in Table7, but they are available upon request.

The FICCI-based Indian Banking Association Report recently showed that corporate loans comprised 62% of total loans, followed by 38% of retail loans during the July-December 2015 period.

Banks' lending policies are a major driver of non-performing loans[START_REF] Mcgoven | Why bad loans happen to good banks[END_REF][START_REF] Sergio | Non-performing bank loans: Cyclical patterns and sectoral risk[END_REF][START_REF] Bloem | The macroeconomic statistical treatment of nonperforming loans[END_REF]. In a seminal study on the Indian banking literature, both[START_REF] Mohan | Transforming Indian banking: In search of a better tomorrow[END_REF] and[START_REF] Reddy | Credit policy, systems, and culture[END_REF] argue that Indian public sector banks' careless lending policies, coupled with the lack of an improved credit culture, are very much crucial in determining the increase of non-performing loans.

Acknowledgements: We would like to thank the editor, Professor Richard Tol (University of Sussex, Brighton, UK), as well as the three anonymous reviewers, for their valuable comments and suggestions that helped us improve our paper significantly. We also thank the participants at the 7 th International Research Meeting in Business and Management (IPAG, Nice, France, 11-12 July 2016). We are also grateful to Jawad Kazmi for his technical assistance. The authors' are responsible for any remaining errors or shortcomings.