Arnaud Beddok
email: a.beddok@gmail.com

Jean-Christophe Faivre

Alexandre Coutte

Jennifer Le Guévelou

Julien Welmant

Jean-Baptiste Clavier

Sébastien Guihard

J-C Faivre

J-B Clavier

G Janoray

V Calugaru

Y Pointreau

A Lacout

J Salleron

M Lefranc

D Hasboun

H M Duvernoy

J Thariat

Practical contouring guidelines with an MR-based atlas of brainstem structures involved in radiation-induced nausea and vomiting

Keywords: nausea, vomiting, brainstem, area postrema, radiotherapy

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Intensity-modulated radiation therapy (IMRT) leads to increased reduction in late toxicities compared to 3-dimensional conformal radiation therapy (3D-CRT) techniques [START_REF] Nutting | Parotid-sparing intensity modulated versus conventional radiotherapy in head and neck cancer (PARSPORT): a phase 3 multicentre randomised controlled trial[END_REF]. In IMRT, dose distributions conform to tumor topography while simultaneously limiting the normal tissue volume exposed to relatively high radiation doses. Therefore, IMRT has become the recommended treatment strategy for many cases of locally advanced head and neck cancer (HNC). However, IMRT treatment plans for HNC typically use a high number of radiation fields and may be associated with new beam-path nontarget tissue toxicities such as anterior oral mucositis, occipital scalp hair loss, headache, nausea ,and vomiting [START_REF] Rosenthal | Beam path toxicities to non-target structures during intensity-modulated radiation therapy for head and neck cancer[END_REF]. Radiation-induced nausea and vomiting (RINV) can occur during the irradiation of a small part of the brainstem called the dorsal vagal complex (DVC) (3). This structure of the medulla oblongata includes the area postrema (AP), the nucleus of the solitary tract (NST), and the dorsal motor nucleus of the vagus (DMV) [START_REF] Miller | The area postrema and vomiting[END_REF]. Several retrospective studies have recently demonstrated a statistical correlation between the grade of RINV and the dose in the DVC [START_REF] Monroe | Factors associated with radiationinduced nausea and vomiting in head and neck cancer patients treated with intensity modulated radiation therapy[END_REF][START_REF] Wang | with Clinical Symptoms of Nausea and Vomiting in Oropharyngeal Cancer (OPC) patients treated with radiation alone using IMRT[END_REF][START_REF] Ciura | Effect of brain stem and dorsal vagus complex dosimetry on nausea and vomiting in head and neck intensity-modulated radiation therapy[END_REF][START_REF] Kocak-Uzel | Beam path toxicity in candidate organs-at-risk: assessment of radiation emetogenesis for patients receiving head and neck intensity modulated radiotherapy[END_REF]. In most of these studies, the location of AP and other structures implicated in RINV were determined on CT scan or MRI by a radiation oncologist with the help of an expert neuroradiologist. However, it remained difficult to accurately identify the true boundaries of these small structures using standard imaging techniques [START_REF] Horsburgh | The circumventricular organs of the brain: conspicuity on clinical 3T MRI and a review of functional anatomy[END_REF]. In the present study, we propose an atlas of RINV-related organs based on pathologic, anatomic, and endoscopic descriptions of the fourth ventricle floor [START_REF] Duvernoy | Sur la vascularisation de l'area postrema et de la face postérieure du bulbe chez l'homme[END_REF][START_REF] Duvernoy | Human Brainstem vessels second edition[END_REF][START_REF] Duvernoy | The circumventricular organs: an atlas of comparative anatomy and vascularization[END_REF][START_REF] Naidich | Duvernoy's atlas of the Human Brain Stem and Cerebellum[END_REF][START_REF] Longatti | The human area postrema: clear-cut silhouette and variations shown in vivo[END_REF][START_REF] Fernandez-Gil | Anatomy of the brainstem: a gaze into the stem of life[END_REF][START_REF] Paxinos | Organization of brainstem nuclei[END_REF]. This atlas was validated as a consensus by an interobserver analysis which compared the delineation of eight different radiation oncologists.

Methods

Population: working group

A multidisciplinary working group (WG) was created to develop these new recommendations for central nervous system (CNS) substructures involved in RINV. All physicians who contributed to the creation of these guidelines are listed as co-authors of this manuscript. The general principles guiding the activities of the WG were: (1) to integrate as accurately as possible anatomic knowledge and previously defined surgical and radiological guidelines into a set of recommendations based on axial CT and MR sections, and (2) to minimize variations in interpretation of the guidelines between oncologists by refining the description of the boundaries at various locations.

Construction of the atlas

A first tutorial was created to help the WG delineate the DVC. We assumed a lack of anatomic knowledge on the part of the radiation oncologists regarding this small structure which is not visible on CT and MR images. This tutorial was created using MRI and CT-scan images of a patient treated with stereotactic radiotherapy for a pituitary adenoma. CT images were obtained using a dosimetric CT with a slice thickness of 0.625 mm. MR images were obtained using a 1.5T magnet. The patient was scanned in the same position as the dosimetric CT, with a slice thickness of 0.5 mm. An image fusion between CT and MR images was performed with studies of pathologic and anatomic surgical descriptions of the brainstem and floor of the fourth ventricle [START_REF] Duvernoy | Sur la vascularisation de l'area postrema et de la face postérieure du bulbe chez l'homme[END_REF][START_REF] Duvernoy | Human Brainstem vessels second edition[END_REF][START_REF] Duvernoy | The circumventricular organs: an atlas of comparative anatomy and vascularization[END_REF][START_REF] Naidich | Duvernoy's atlas of the Human Brain Stem and Cerebellum[END_REF][START_REF] Longatti | The human area postrema: clear-cut silhouette and variations shown in vivo[END_REF][START_REF] Fernandez-Gil | Anatomy of the brainstem: a gaze into the stem of life[END_REF][START_REF] Paxinos | Organization of brainstem nuclei[END_REF]. The location, size, and true boundaries of the DVC were then determined with the help of three experts in brainstem anatomy (Pr H.M. Duvernoy, Dr D. Hasboun, Dr M. Lefranc). The three parts of the brainstem are rarely delimited, so this study was also an opportunity to find a consensus on this subject.

Secondly, each member of the working group delineated the DVC and the three parts of the brainstem in a CT set from one patient treated for nasopharyngeal neoplasm (patient A). The CT images (1-mm slice thickness) were carried out with patient in a supine position on a multidetector-row spiral CT scanner. A fusion between MR and CT images was necessary to determine the location of the DVC.

The contours of all radiation oncologists were compared (see the method of comparison below).

Subsequently, the outcome of this procedure was individually presented to and discussed with each observer in order to identify the most frequent inconsistencies and to formulate consensus guidelines.

Finally, consensus delineations were depicted on axial T2-weighted MRI in an atlas of structures involved in RINV with a 1-mm slice thickness (atlas in supplementary data). A second inter-observer analysis was performed to confirm validity and reproducibility of this atlas. In this second analysis, each member of the working group delineated the DVC and the three parts of the brainstem in a CT set from the same patient A and from another patient B.

Methods of comparison

These methods were used for the first (Time 1) and the second (Time 2) interobserver analysis. The evaluation of the variability of contours was benchmarked using a "reference" contour (delineated with the help of the three experts of the brainstem: HMD, DH, and ML). ARTISTRUCT™ software version 1.0 (AQUILAB®, Lille, France) was used to evaluate and compare the contours, both qualitatively and quantitatively. For the first step, each volume submitted by each of the eight radiation oncologists in the WG (DVC, midbrain, pons, and medulla oblongata) was compared with the reference volume proposed by the three experts. In the second step, different indices reflecting the correlation of the volume with the "experts reference contour" for all volumes were calculated. Three of these indices are based on volume ratios: the Volume Ratio (VR) between the volume of one of the eight radiation oncologists and the volume of the experts, the Common Delineated Volume (CDV), and the Additional Delineated Volume (ADV). Then, the kappa index (KI) and the Overlap Volume (OV) were calculated.

Results

After the working group (WG) delineated the DVC and the three parts of the brainstem in the patient A, a first interobserver analysis (Time 1) was performed and the variability in delineation was discussed.

Subsequently, the WG agreed on consensus definitions for these structures and formulated the final consensus guidelines for these structures implicated in RINV (available in supplemental data). A second interobserver analysis (Time 2) confirmed that the delineation of the DVC and the three parts of the brainstem was reproducible. In the following sections, each of these structures will be briefly reviewed with a short description of their main anatomic boundaries. The boundaries correspond to a patient lying supine with his head in a ''neutral'' position. The boundaries are summarized in Table 1.

Midbrain

The midbrain is the most cranial structure of the brainstem. The cranial limit is the cranial border of the cerebral aqueduct. This limit is clearly visible in the sagittal plane of the brainstem in MRI. The caudal limit is below the inferior coliculli. The midbrain is also limited anteriorly by the interpeduncular cistern and posteriorly by the quadrigeminal cistern.

Pons

The pons is similar to a bridge connecting the right and left cerebellar hemispheres. The cranial limit (which is also the caudal limit of the midbrain) is above the inferior coliculli of the midbrain. Posteriorly, the pons is limited by the floor of the fourth ventricle. Convincing arguments have led us to include the superior, middle, and inferior cerebellar peduncles in the pons structure because the dose constraints are the same as those for the pons.

Medulla oblongata

The medulla oblongata connects the spinal cord to the pons. The caudal limit of the medulla oblongata is located at the level of the foramen magnum (clearly visible on a sagittal view of the brainstem on a CT image). The superior half of the posterior surface of the medulla oblongata is the floor of the fourth ventricle, above the median aperture. This surface is characterized by a median sulcus, clearly visible on CT and MRI. Under the median aperture, which is also the cranial limit of the central canal, this median sulcus is not visible. The posterior border of the medulla is then the posterior cerebellomedullary cistern, whereas the lateral borders are limited by the lateral apertures and the lateral cerebello-medullary cisterns.

Dorsal vagal complex (DVC)

The dorsal vagal complex (DVC), consisting of the AP and the parts of the NST and the DMV connected to the AP, is located in the dorsal brainstem, in the floor of the fourth ventricle. The DVC integrates sensory and motor information of neurovegetative functions, including vomiting. In a posterior view, the floor of the fourth ventricle appears rhomboidal (Fig 1). The back surface of the pons and the superior half of the dorsal surface of the medulla oblongata form the floor of the fourth ventricle. Its superior angle is continuous with the cerebral aqueduct, while its inferior angle is continuous with the central canal. The floor is grooved vertically by a median sulcus. The area postrema (AP) is attached to the inferior angle of the floor of the fourth ventricle, overlying the lower portion of the vagal trigone and facing the Magendie foramen. In an endoscopic view, the AP is directly visible from the back through the median aperture (foramen of Magendie) and is joined by the opening of the central canal in the fourth ventricle [START_REF] Longatti | The human area postrema: clear-cut silhouette and variations shown in vivo[END_REF]. The AP has the appearance of arched wings opened on either side of the floor of the fourth ventricle, facing the Magendie foramen, and diverging upward from the central canal. The caudal limit of the DVC is easily determined on a sagittal view of the brainstem and must be delineated in the transversal plane of the obex, which is also in the plane of the central canal aperture (Fig. 1A, 1B) ( 16). This limit is a unique volume located at a distance of 52 ± 3.5 mm from a line between the head and the body of the corpus callosum (Fig. 1C,1D). The DVC is located on both sides of the median sulcus of the medulla oblongata (Fig 2A ,2B). In order to be more reproducible, we proposed delineating a 4-mm diameter circle which included the different structures of the DVC (Fig. 2C). The cranio-caudal length of the AP is 5 mm (10).

Tables 2 and3 show the results of the first and second interobserver analyses.

Volume comparison

In the first analysis (Time 1), the DVC delineated by the experts for the patient A was measured at 0.04 cm 3 and extended 0.4 cm in the vertical plane. The average volume and height of the DVC delineated by the working group (WG) for the same patient A were significantly different from that of the experts with a result of 0.14 cm 3 (p = 0.02) and 0.64 cm (p = 0.008), respectively.

In the second analysis (Time 2), the volume of Patient A's DVC delineated by the expert was 0.14 cm 3 and extended 0.5 cm in the vertical plane. The average volume and height of Patient A's DVC delineated by the WG were not significantly different from that of the expert with a result of 0.14 cm 3 (p = 0.52) and 0.5 cm (p = 1), respectively. The analysis of the patient B confirmed that the DVC volume and height of the WG did not differ from the expert. The patient B also allowed us to validate the reproducibility of the midbrain, the pons, and the medulla oblongata delineations (Table 3).

Indices comparison

In the first analysis (Time 1), the different indices obtained by the WG compared to the expert for the patient A were 3.29 for the Volume Ratio (VR), 74% for the Common Delineated Volume (CDV), 72%

for the Additional Delineated Volume (ADV), 0.26 for the Overlap Volume (OV), and 0.40 for the Kappa Index (KI). In the second analysis (Time 2), the different indices obtained by the WG compared to the expert for the same patient A were 1 for the VR, 67% for CDV, 33% for the ADV, 0.53 for the OV, and 0.67 for the KI. The results obtained in the second analysis were significantly better than these ones obtained in the first analysis (Table 2 shows the mean differences for each index). These results showed a significant improvement in the reproducibility of the DVC delineation and validated the second tutorial as an atlas. The indices obtained by the WG compared to the expert for patient B are reported in Table 2 for the DVC and in Table 3 for the three parts of the brainstem.

Discussion

Radiation-induced nausea and vomiting (RINV) were rare side effects with 3-dimensional conformal radiation therapy (3D-CRT) techniques. RINV seems to be more frequent since the development of new technologies such as intensity-modulated radiation therapy (IMRT) for the treatment of locally advanced head and neck cancer (HNC). The RINV atlas described above has thus been designed to prospectively assess dose-volume effects for structures previously identified as being involved in RINV.

In 2008, Rosenthal et al observed in a post hoc analysis that the reported rates of emesis (4%) and nausea (4%) in patients treated with 3D-CRT were substantially lower than those observed in their IMRT patient series. In recursive partitioning analysis (RPA), nausea and emesis were associated with the reconstructed mean dose to the brainstem of >36 Gy [START_REF] Rosenthal | Beam path toxicities to non-target structures during intensity-modulated radiation therapy for head and neck cancer[END_REF]. Other previous studies on stereotactic radiosurgery have shown that the dose to the area postrema (AP) in the brainstem correlated with nausea and vomiting and that limiting the dose to even lower levels could reduce the incidence of these toxicities (3). Therefore, the MDA team surmised that RINV may be related to the dose in the dorsal vagal complex (DVC) which includes the AP. Ciura et al. in 2011 showed a positive relationship analysis between severe (grade 3 or greater) toxicity (nausea and vomiting) and brainstem mean dose and AP mean dose on logistic regression (p ≤0.002) [START_REF] Ciura | Effect of brain stem and dorsal vagus complex dosimetry on nausea and vomiting in head and neck intensity-modulated radiation therapy[END_REF]. In another study, the same team demonstrated that a dose-response association was detectable for AP (p = 0,015) and after multiple statistical analyses (RPA, multivariate analysis), V24 (percentage of volume receiving at least 24 Gy) to the AP <76% was proposed to decrease the risk of RINV [START_REF] Kocak-Uzel | Beam path toxicity in candidate organs-at-risk: assessment of radiation emetogenesis for patients receiving head and neck intensity modulated radiotherapy[END_REF]. Moreover, several other retrospective studies have recently demonstrated a statistical correlation between frequency and grade of RINV and the dose in the DVC. In 2008, Monroe et al. observed that the median dose to the DVC was significantly higher in patients with grade 1-2 nausea than in those with no RINV. In univariate analysis, patients with no acute nausea had a median DVC dose of 6.5 Gy, whereas, those with grade 1-2 nausea had a median DVC dose of 26.9 Gy (p = 0.04) [START_REF] Monroe | Factors associated with radiationinduced nausea and vomiting in head and neck cancer patients treated with intensity modulated radiation therapy[END_REF]. However, the relation between the frequency and the grade of RINV and the dose in the DVC has neither been demonstrated in a multivariate analysis nor in the nasopharyngeal cancer, Lee et al. demonstrated that the dose to the DVC was not predictive of RINV [START_REF] Lee | Dosimetric predictors of radiation-induced acute nausea and vomiting in IMRT for nasopharyngeal cancer[END_REF]. The lack of evidence between RINV and dose in the DVC could be explained by the lack of consensus for the delineation of the DVC. An accurate delineation would allow more reproducible and real results. Thus, providing atlas-based guidelines for the delineation of RINV structures appears relevant to routine practice. Not only can it be used to further define dose volume and toxicity relationships that may be used for NTCP modeling, but it also can be used to apply new OAR constraints for treatment optimization when needed.

Despite high resolution and many new sequences, the DVC is barely visible on high resolution MRI and is not visible on planning CT scans. In a previous study, Williams et al. described the visualization of enhancement in the area postrema in a case report by using MR imaging [START_REF] Williams | Demonstration of the area postrema with contrast-enhanced MR[END_REF]. However, Horsburgh et al. have recently studied the normal appearance and contrast enhancement of CVO on routine clinical brain MRI images. The AP was enhanced in only 2% of their cases [START_REF] Horsburgh | The circumventricular organs of the brain: conspicuity on clinical 3T MRI and a review of functional anatomy[END_REF]. Therefore, the present atlas study was carried out using microscopic and endoscopic descriptions of the DVC (10-16). We have determined boundaries for different organs implicated in RINV with easy anatomic landmarks. In order to test the reproducibility of the DVC delineation, we have performed two interobserver analyses.

The eight radiation oncologists of the working group have delineated the DVC, for the first time, by using a first tutorial as model. The DVC on this tutorial was very similar to the real DVC, particularly regarding the small size and volume (0.4 cm and 0.04 cm 3 , respectively). Nonetheless, we obtained poor reproducibility (KI = 0.4). Then, following discussions, we decided to slightly expand the volume of the structure (by using a circle of 4 mm in diameter) and to accurately define the caudal limit (the transversal plane of the obex). The second interobserver analysis showed better results (KI = 0.70) and validated the second tutorial as a practical contouring guideline to be used for an MR-based atlas for the delineation of the DVC. The three parts of the brainstem have rarely been delimited, so this study was also an opportunity to find a consensus on this subject.

The strength of our study is that it is multicentric and multidisciplinary. Indeed, the ten radiation oncologists came from seven different French departments of Radiation Oncology and the atlas was validated by a neuroanatomopathologist (HMD), a neurosurgeon (ML), a neuroanatomist, and neuroradiologist (DH). Nevertheless, this contouring atlas is based on MRI so limits the cases to those for which MR is available. Finally, this RINV atlas may be used in routine practice and will have to be further evaluated in different clinical situations using different irradiation techniques. The atlas may be used in prospective studies and in routine use to share better knowledge of these organs with the aim of improving the quality of life of patients after irradiation for head and neck cancer.

  Patient A's indices of the Time 1 were compared with Patient A's indices of the Time 2 (17). Another patient (Patient B) allowed us to confirm the validity of the second tutorial as an atlas for the delineation of the DVC. The delineation of the three parts of the brainstem was also analyzed and reported for Patient B. Statistical analysis Delineation evaluation was conducted prospectively and comparatively. Statistical analysis was performed with R-Studio software (GNU Affero General Public License v3 2007). Comparison between the volume obtained (eight radiation oncologists of the WG) and the experts' volume was done with the Student t-test. Comparative analysis of Patient A's indices of the Time 1 and Patient A's indices of the Time 2 after was done with the Student t-test for paired samples. Values of two-sided p <0.05 were considered statistically significant.

Fig. 1 :

 1 Fig. 1: Caudal limit of the dorsal vagal complex

Fig. 2 :C 2 A

 22 Fig. 2: Transversal view of the medulla oblongata at the level of the Dorsal Vagal Complex

Fig. 3 :B

 3 Fig. 3: Comparison between the first and second interobserver analysis for the Dorsal Vagal Complex delineation in Case 1

Table 1 :

 1 Organs affected in RINV with specification of anatomic boundaries : dorsal vagal complex. AP: area postrema. NST: nucleus of the solitary tract. DMV: dorsal motor nucleus of the vagus

	Borders
	per region

DVC

Conflicts of interest: None declared

Acknowledgments: The authors acknowledge AQUILAB SAS for their contribution to this study.


Nawaz Kishwar
email: kishwar.nawaz@etu.univ-orleans.fr

Leo

Amine Lahiani
email: amine.lahiani@univ-orleans.fr

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

Natural Resources as Blessings and Finance-Growth Nexus: A Bootstrap ARDL Approach in An Emerging Economy

Keywords: Financial Development, Natural Resources, Economic Growth, blessings hypothesis JEL Classification: O13, O40, C12, C22

This paper examines the link between financial development and economic growth in Pakistan by considering important role of natural resources in production function for the period of 1972-2017. Capital and labour are additional contributing factors of economic growth. To determine the integrating properties of the variables, we apply SOR unit root test containing information for sharp and smooth structural breaks in the series. We also employ the bootstrapping ARDL bounds testing approach to examine the cointegration between the factors of production. The causal relationship between financial development, natural resources, capital, labour and economic growth is tested by applying the VECM Granger causality test in the presence of structural breaks.

The empirical findings indicate that financial development, natural resources, capital, labour and economic growth are cointegrated for long-run association. Additionally, financial development enhances domestic production as well as economic growth. Natural resources as blessings hypothesis is validated. Capital and labour also add to economic growth. The VECM Granger causality test results show the bidirectional causal relationship between financial development and economic growth. The feedback effect also exists between natural resources and economic growth. This paper helps policy makers in designing a comprehensive policy for strengthening finance-growth by using natural resources as an economic tool.

Introduction

Every country needs an efficient and sound financial system to stimulate economic activity. A developed and sound financial system enables an economy to stimulate economic growth [START_REF] Pradhan | The dynamics of insurance sector development, banking sector development, and economic growth: Evidence from G-20 countries[END_REF]. In Pakistan, financial system is denominated by banking sector, which constitutes 88% of the share of total financial sector, which comprises 34 commercial banks and 4 specialized banks. The share of the non-banking sector is almost 12%, which includes Mudarbas, insurance companies, housing finance companies, leasing companies, mutual funds companies, and venture capital companies. Moreover, Pakistan's stock market is considered a developing and emerging stock market. Both of the markets (i.e., capital and money) set investment opportunities to enhance economic growth. The financial system of Pakistan remains resilient while facing uncertainties at local and international levels. These uncertainties include continuous fiscal deficit, an energy crisis, financial instability, and security challenges. The country has relied heavily on the banking sector, which has caused insufficient external inflows, due to heavy debt payments and the continuous falling of foreign reserves (Government of Pakistan, 2018).

Pakistan is located in South Asia, and its land contains important and valuable natural resources, such as natural gas, oil, hydro power potential, coal, iron, copper, salt, and limestone. Pakistan has 175 billion tons of coal reserve, which is equal to 618 billion barrels of crude oil (Government of Pakistan, 2018). This coal, known as black gold, is more than double the value of oil reserves held by top four richest countries. These coal reserves have the ability to produce electricity for more than 200 years. Pakistan's 885.3 billion cubic meters of natural gas reserves can be utilized for the next 20 years. Pakistan contains gold/copper reserves in Saindak along with rock salt in the Pothohar Plateau. Mineral resources include gypsum, uranium, limestone, chromite, iron ore, rock salt, silver, precious stones, gems, marbles, tiles, sulphur, fire clay, and silica sand. In addition to its diverse natural resources Pakistan's growing urban middle class and intelligent youth population are likely to make it an important hub of culture, knowledge, and wealth. This crucial human aspect along with its natural resources will lead to long-run economic development in Pakistan (Government of Pakistan, 2018). Financial development can affect economic growth in four ways: First, investors invest their money in long-term investment projects due to the potential impact of liquidity shocks on financial markets. Long-run investment projects tend to be less affected by liquidity shocks. The investors invest their money in different modes of financing, thus diversifying the risk. This tool is called "financial intermediaries facilitate pooling and trading of risk" (Obstfeld, 1994).

Second, on the basis of ex-ante information, financial mediators advise investors how to efficiently allocate resources. There exists an association among finance, entrepreneurship, and economic growth (King and Levin, 1993). According to King and Levin (1993), monetary structure manipulates productivity-enhancing activities via prospective entrepreneurs. This theory implies that the cost of investing in productivity-enhancing activities becomes lower, leading to economic growth [START_REF] Levine | Finance and growth: theory and evidence[END_REF]. Third, the development of stock markets efficiently endows corporate control and equity markets align the interests of management and firm owners [START_REF] Nieuwerburgh | Stock market development and economic growth in Belgium[END_REF]. Fourth, financial markets provide the opportunity to organize the savings to bring in more profits which of course, fosters economic growth [START_REF] Nieuwerburgh | Stock market development and economic growth in Belgium[END_REF]. [START_REF] Sachs | How to handle the macroeconomics of oil wealth[END_REF] notes that natural resources may positively affect economic growth. For instance, oil and gas extraction revenues lead to real income which in return, improves earnings for the poorer population segment, which can improve living standards. Income from natural resources helps in finance public and private consumption and generates possibilities of borrowing money from financial institutions for capital investments. The income from natural resources can finance core public goods to stimulate economic growth. A well-developed infrastructure strengthens the domestic financial sector, which boosts economic activity. This shows that natural resources affect economic growth directly via capitalization and indirectly via financial development. [START_REF] Badeeb | Oil curse and finance-growth nexus in Malaysia: The role of investment[END_REF] note that natural resources are ruminated as additional sources for financial institutions, which can enhance the finance-growth linkage. This paper is contributing in the following five ways: (i), It is a pioneering effort to examine the relationship between financial development and economic growth using annual Pakistani data from 1972 to 2017 by considering the vital role of natural resources in augmented production.

(ii), We have developed a financial development index using the principal component analysis.

The sub-indices of the financial development index include broad money as a share of GDP captures of an economy's money supply; domestic credit to the private sector shows the allocated savings level for productive investment projects; bank nonperforming loans as share of total loans; stock market capitalisation as the share of GDP which indicates the size of a stock market; and stock market traded value and stock market turnover illustrate the profitability in stock markets. (iii), This study examines the unit root properties of financial development, economic growth, natural resources, capital, and labour by applying the sharp and smooth structural break unit root test developed by Shahbaz et al. (2018). (iv) In order to examine cointegration, we apply the bootstrapping ARDL bounds testing approach to validate the existence of a cointegration relationship amid the variables. (v), We employ the bootstrapping based VECM Granger causality approach to examine the causal linkages among the variables.

Empirical evidence reveals that financial development boosts economic growth. Natural resources are also positively linked with economic growth, which validates that natural resources are blessings. Capital and labour add to economic growth. The causality test indicates the presence of a feedback effect between financial development and economic growth. Natural resources and economic growth are interdependent. A bidirectional causal relationship also exists between financial development and natural resources.

The rest of the paper is divided into five sections: Section 2 reviews the related literature. Section 3 describes the data, empirical modelling and methodology. Section 4 discusses the results; and

Section 5 provides conclusion with policy implications.

Literature Review

This paper addresses the finance-growth nexus by incorporating natural resources as an additional determinant of financial development and economic growth. In doing so, we divide the literature review section into three sub-sections: financial development and economic growth nexus; natural resources and economic growth nexus; and natural resources, financial development, and economic growth nexus.

Financial Development and Economic Growth Nexus

Plenty of studies in the existing literature use various measures of financial development to investigate the relationship between financial development and economic growth and provided mixed empirical findings. This finance-growth debate goes back to [START_REF] Schumpeter | The theory of economic development[END_REF], who indicates that financial development plays an important role in promoting economic activity via capitalisation. Later, McKinnon and Shaw (1973) highlight that financial system spurs economic growth by financial liberalisations. Furthermore, [START_REF] Lee | Once again, is openness good for growth?[END_REF] indicate that financial development helps move capital from the developed world to developing economies, which enhances domestic production and adds to economic growth. [START_REF] Shan | Does Financial Development Lead Economic Growth? The Case of China[END_REF] conclude that financial development positively impacts economic growth. On the other hand, [START_REF] Gantman | A fragile link? A new empirical analysis of the relationship between financial development and economic growth[END_REF] note that financial development does not significantly impact economic growth. However, [START_REF] Law | Does too much finance harm economic growth?[END_REF] report that financial development impedes economic growth due to unproductive and wasteful use of domestic credit.

For individual country analyses, studies present ambiguous empirical findings. For instance, [START_REF] Masih | Causality between financial development and economic growth: An application of vector error correction and variance decomposition methods to Saudi Arabia[END_REF] focus on export production to examine financial development and economic growth. They apply the variance decomposition approach and find that financial development, exports, and innovative shocks contribute to economic growth by 34.93%, 13.15%, and 41.89%, respectively. Their empirical evidence indicates the presence of a supply-side hypothesis, as financial development causes economic growth. [START_REF] Hye | Economics, finance and development in China: Johansen-Juselius cointegration approach[END_REF] report the positive impact of financial development on economic growth in China. [START_REF] Uddin | The causal nexus between financial development and economic growth in Kenya[END_REF] apply the production function to investigate the nexus between financial development and economic growth in Kenya from 1971 to 2011. Their findings suggest a cointegration between the variables and that financial development is the main contributor to economic growth in the long and short run 1 . [START_REF] Polat | Revisiting linkages between financial development, trade openness and economic growth in South Africa: fresh evidence from combined cointegration test[END_REF] report the positive role financial development plays in augmenting economic growth in South Africa. Further, they note that economic growth Grangercauses financial development, leading to trade openness. On the contrary, [START_REF] Shahbaz | Re-visiting financial development and economic growth nexus: The role of capitalization in Bangladesh[END_REF] found that financial development encourages loans at cheaper costs, spurring capitalisation, which in turn boosts domestic output and increases economic growth in Bangladesh. Moreover, their results indicate a feedback effect between financial development and economic growth. [START_REF] Kandil | The drivers of economic growth in China and India: globalization or financial development?[END_REF] reinvestigate the main determinants of economic growth for India and China, finding that financial development positively affects economic growth.

1 [START_REF] Tinoco-Zermeno | Growth, bank credit, and inflation in Mexico: evidence from an ARDL-bounds testing approach[END_REF] explore the relationship between economic growth, bank credit (i.e. financial development), and inflation in Mexico using the bounds testing approach. They note that the variables cointegrated for the long run. Their empirical exercise indicates financial development has a positive effect on economic growth.

In the case of Pakistan, only a few studies have investigated the relationship between financial development and economic growth by using various measures of financial development. For example, [START_REF] Husain | The stock market and the economy in Pakistan[END_REF] revisit the impact of financial market development (measuring by stock market prices) on economic growth and find that stock market development plays an insignificant role in enhancing economic growth. Later, [START_REF] Khan | Financial development and economic growth: The case of Pakistan[END_REF] report that financial depth and real interest rate positively contribute to economic growth, but the impact of investment is insignificant. [START_REF] Khan | Trade, financial and growth nexus in Pakistan[END_REF] incorporate trade openness in the production function and find that trade openness strengthens the finance-growth relationship, but in the short run, this openness impacts economic growth minimally. Shahbaz et al. (2008a) investigate the contributing factors of economic growth. They note that foreign direct investment and foreign remittances strengthen the nexus between financial development and economic growth2 . [START_REF] Jalil | Impact of financial development on economic growth: empirical evidence from Pakistan[END_REF] re-examine the finance-growth nexus from 1975 to 2008 by using a PCA-generated financial development index. Their results validate the existence of cointegration and showed that financial development spurs economic growth. [START_REF] Anwar | Relationship between financial sector development and sustainable economic development: Time series analysis from Pakistan[END_REF] report that financial development plays an important role in stimulating economic growth via capitalisation, and a supply-side effect is validated. [START_REF] Shahbaz | Does trade openness affect long run growth? Cointegration, causality and forecast error variance decomposition tests for Pakistan[END_REF] argue that trade openness leads to financial development, which further leads to economic growth. This confirms the validity of a supplyside effect, while a demand-side effect is not valid.

Natural Resources and Economic Growth Nexus

Scholars have debated the "resources curse" hypothesis for more than three decades. The resources curse hypothesis reveals that resources-rich countries experience lower economic growth compared to countries with no primary resources. This hypothesis argues that natural resources do not stimulate economic growth according to classical growth theory [START_REF] Ascher | Why Governments Waste Natural Resources: Policy Failure in Developing Countries[END_REF]. [START_REF] Sachs | Natural resource abundance and economic growth[END_REF] note that corruption, inefficient bureaucracy, and poor management restricting growth in resource-abundant countries. In such situations, resource abundance is linked with high income inequality due to corruption and mismanagement, which impedes economic growth. This shows that government wastes natural resources, i.e. minerals, oil, metals and energy [START_REF] Rutland | Russia as an energy superpower[END_REF]. There are many studies that validate the resource curse hypothesis, such as Arezki and van der Ploeg (2011) and [START_REF] Badeeb | Oil curse and finance-growth nexus in Malaysia: The role of investment[END_REF].

Economists, such as Adam Smith and David Ricardo, also observe that valuable natural resources positively affect growth development. Many other post-war economic analysts also supported this phenomenon in the 1970's. [START_REF] Rostow | The Stages of Economic Growth: A Non-Communist Manifesto[END_REF] summarizes that natural resources endowment would improve fundamental change in under-developed economies by creating new market and investment opportunities, citing countries such as Britain, the US, and Australia [START_REF] Viner | America's aims and the progress of underdeveloped countries[END_REF]. [START_REF] Brunnschweiler | Cursing the blessings? Natural resource abundance, institutions, and economic growth[END_REF] find that natural resource abundance stimulates economic growth via institutional quality. [START_REF] Mehrara | Reconsidering the resource curse in oil-exporting countries[END_REF] report that oil revenues are positively linked to economic growth; in other words, natural resources are blessings. Similarly, [START_REF] Philippot | Natural Resources and Economic Development in Transition Economies[END_REF] also negates the resource curse hypothesis and found that natural resources are blessings in transitory countries. [START_REF] Zagozina | The Resource Curse Paradox: natural resources and economic development in the former Soviet countries[END_REF] also reports that natural resources, such as agricultural exports, minerals, and fuel exports add to economic growth in Soviet countries. Furthermore, [START_REF] Koitsiwe | Relationship between mining revenue, government consumption, exchange rate and economic growth in Botswana[END_REF] find that change in mining revenues is responsible for economic growth variation as mining revenues Granger-cause economic growth. [START_REF] Moshiri | Natural Resources, Institutions Quality, and Economic Growth; A Cross-Country Analysis[END_REF] consider the importance of institutional quality while investigating the effect of natural resources on economic growth using cross-country data for 1996-2010. They find that natural resource wealth is positively linked to economic growth, but institutional quality insignificantly impacts economic growth. Recently, findings of [START_REF] Arin | The resource curse revisited: A Bayesian model averaging approach[END_REF] negate the natural resource curse and confirm that natural resources have a positive effect on economic growth, but it is linked with better quality of institutions.

Natural Resources, Financial Development and Economic Growth Nexus

Existing empirical literature includes various studies which consider financial development as an important determinant of economic growth while investigating the resources-growth nexus. For example, [START_REF] Satti | Empirical evidence on the resource curse hypothesis in oil abundant economy[END_REF] examine the resources-growth nexus in the Venezuelan economy by including financial development in an augmented production function. They note that all the variables are cointegrated for the long-run relationship. Their empirical analysis further exposes that financial development could not nullify the negative effect of natural resources on economic growth. In Yemen, Badeeb and Lean (2017a) find that natural resources are negatively linked with economic growth, but a developed financial sector can transform that curse into a blessing with the proper allocation of domestic savings into productive investment ventures. Badeeb and Lean (2017b) find a long run relationship between sectoral growth and its determinants. Their empirical findings further reveal that natural resources have a negative impact on agricultural and manufacturing sectors, confirming what has been coined the "Dutch Disease".

On the contrary, [START_REF] Quixina | Financial development and economic growth in a natural resource based economy: Evidence from Angola[END_REF] use oil revenues as a measure of natural resources and assess financial development as an important determinant of economic growth. They find that natural resources lead to economic growth, but financial development is ineffective in promoting economic growth. [START_REF] Badeeb | Oil curse and finance-growth nexus in Malaysia: The role of investment[END_REF] find that oil dependence has a positive but weak effect on economic growth via investment, but financial development is negatively linked with economic growth. [START_REF] Rustamov | Oil production cost, financial development, and economic growth in Russia[END_REF] investigate the relationship between oil production cost, financial development, and economic growth for the Russian economy. They also include oil prices, natural gas prices, education, and effectiveness of public institutions by applying the [START_REF] Toda | Statistical inference in Vector Autoregressions with possibly integrated processes[END_REF] Granger causality test. They find that natural gas prices and financial development lead to positive economic growth.

Empirical Modelling, Data and Methodology

Empirical Modelling

Existing applied economics literature is full of studies investigating the association between financial development and economic growth using time series, cross-sectional, and panel data sets [START_REF] Adu | Financial development and economic growth in Ghana: Does the measure of financial development matter?[END_REF]. The natural resources-finance-growth nexus is an open question, not only for researchers but also for academicians and practitioners when considering appropriate policy guidelines to achieve sustainable long-run economic development [START_REF] Al-Yousif | Financial development and economic growth: another look at the evidence from developing countries[END_REF][START_REF] Khan | Financial development and economic growth in Pakistan: evidence based on Autoregressive Distributed Lag (ARDL) approach[END_REF]. Studies in existing literature provide ambiguous findings due to specification problems and different sample sizes for empirical analysis.

The debate on growth accounting goes back to [START_REF] Solow | A Contribution to the Theory of Economic Growth[END_REF], who developed a growth model by assuming that saving rates, population growth, and technological development play vital roles in enhancing domestic production. This growth model presents a comprehensive but simple picture of growth accounting and explains why economies have different income levels. Solow's model is criticized due to the constant nature of economic growth determinants [START_REF] Acemoglu | Introduction to Modern Economic Growth[END_REF].

Later, [START_REF] Mankiw | A contribution to the empirics of economic growth[END_REF] augment the production function by introducing human capital as an important factor of domestic production. The general form of the production function is as follows:

= with 0 < < 1 (1)
where capital stock ( ) and labour ( ) are the main determinants of domestic production ( ), while technological development ( ) also contributes to economic growth by stimulating economic activity. We augment the Cobb-Douglas production function by incorporating financial development, capital, and labour. It is assumed that technological advancement is affected by financial development, skilled human capital, and the openness of an economy.

Financial development stimulates economic activity via capitalisation and provides financial resources at cheaper production costs by importing advanced technology from developed countries [START_REF] Shahbaz | Does trade openness affect long run growth? Cointegration, causality and forecast error variance decomposition tests for Pakistan[END_REF]. This indicates that financial development plays a key role in increasing returns to scale and inter-sectoral specialization and strengthening the trade flow structure by improving financial intermediation [START_REF] King | Finance and growth: Schumpeter might be right[END_REF][START_REF] Levine | Financial development and economic growth: Views and agenda[END_REF].

Financial sector development helps an economy reap the fruits from technological development,

and globalisation provides access to advanced technology, which affects domestic production.

Globalisation via trade flows, as well as foreign capital flows, contributes to economic growth.

Natural resources also contribute to economic growth directly and indirectly. Natural resources directly contribute to economic growth via several ways: (i) Natural resources are a source of income to finance public and private consumption. (ii) Natural resources are helpful in financing public investment ventures. (iii) Income from natural resources is a sustainable financial source for core public goods such as infrastructure, etc. Natural resources expedite economic growth process by stimulating consumption and investment activities [START_REF] Adams | The role of country-level institutional factors in escaping the natural resource curse: Insights from Ghana[END_REF]. Indirectly, natural resources contribute to economic growth via financial development. Financial sector is helpful in transforming natural resources revenues into productive investment projects [START_REF] Law | Financial Development and Oil Resource Abundance -Growth Relations: Evidence from Panel Data[END_REF], which stimulates economic activity. In doing so, Shahbaz et al. (2018a) argue that a well-developed financial sector is a way to escape natural resource curse. It seems that exclusion of financial development when investigating the relationship between natural resources and economic growth may provide ambiguous empirical evidence. The above discussion directs us to construct a general model as follows:

= ∅. ( 2
)
where ∅ is a time-invariant constant, is a measure of natural resources, and indicates financial development. Substituting Equation 2with Equation 1 and incorporating capital and labour as additional contributing factors of economic growth, we formulate a general form of the Cobb-Douglas production function as follows:

= ∅. (3)
We have divided Equation 3 by population on both sides and then transformed all of the variables into a natural logarithmic. Equation 3 can be constructed for empirical purposes as follows:

= + + + + + (4)
where and refer to natural logarithm and error term, respectively. Economic growth is measured by real GDP per capita, financial development is produced using the PCA technique, natural resources are calculated as real natural resources revenue per capita, and capitalisation denotes real gross fixed capital formation per capita. Natural resources, capitalisation and labour are factors of production. Financial development affects economic growth by activating investment opportunities. Financial stability is key element for sustainable economic growth in long run [START_REF] Nasir | Financial and economic stability as 'two sides of a coin': Non-crisis regime evidence from the UK based on VECM[END_REF].

The Data Collection

This study uses time series data from 1972 to 2017. The data on real GDP (constant 2010, LCU),

Financial Development Index

Existing applied economics literature fails to provide a unique measure of financial development. properly defines financial development. All of the measures may be highly correlated and show the problem of multi-collinearity (Adu et al., 2013, Tyavambiza and[START_REF] Tyavambiza | Financial and monetary reforms and the financegrowth relationship in Zimbabwe[END_REF]. PCA is mostly used to overcome the issue of multi-collinearity, as well as the degree of freedom, by generating an appropriate index for a particular variable. For example, in our case, broad money !" as a share of GDP captures money supply in an economy, domestic credit to private sector ( ) shows the allocated savings level to private sector for productive investment projects, bank nonperforming loans as share of total loans (!# ), stock market capitalisation indicates the size of a stock market (" ), while stock market traded value ($ ) and stock market turnover ($ ) illustrate the profitability in stock markets. There may be a problem of multicollinearity if these variables are used together in a regression analysis [START_REF] Polat | Revisiting linkages between financial development, trade openness and economic growth in South Africa: fresh evidence from combined cointegration test[END_REF] 3 .

These correlated variables are transformed into small uncorrelated variables without any change in variations of the original data by using a PCA4 .

In the lower segment of The presence of a high correlation amid the variables may be a cause of multi-collinearity while using these variables simultaneously in a regression analysis to capture financial development.

The presence of multi-collinearity not only weakens the empirical analysis, but it also makes the analysis inefficient. This issue is solved by applying a PCA to generate an index of financial 1).

SOR Unit Root Test with Sharp and Smooth Breaks

We use the sharp and smooth structural breaks unit root test developed by Shahbaz, Omay, and Roubaud (hereafter SOR, 2018b) to examine the integrating properties of our variables.

Although various unit root tests are available for testing the stationarity properties of variables, such tests provide biased empirical results due to their low explanatory power (Shahbaz et al., 2018b). The uniqueness of SOR is that it is a nonlinear-unit root test accounting for sharp and smooth breaks occurring in a times series. The SOR test (following [START_REF] Leybourne | Unit roots and smooth transitions[END_REF] entails a two-step approach which is as follows:

Step-1. This step involves using the constrained nonlinear optimization algorithm via genetic algorithm5 . Subsequently, the deterministic component of the preferred model is estimated, and its residuals are calculated using Models A, B, and C as shown below:

Model A: ̂ = & -( ) -( ) * ), ,̂ (4)
Model B:

̂ = & + ( ) + -+ ( ) *, . ,̂ (5)
Model C:

̂ = & -( ) ---( ) * ), ,̂ -- * ), ,̂ (6)
Step-2. Here we compute the Enders and Lee, hereafter, EL (2012) test statistic, that is the t-ratio associated with ∅ / in OLS regression:

̂ = 0 + ∅ + 1 (7)
where 0 is a deterministic function of and 1 is a stationary disturbance with constant variance 2 , noting that is weakly dependent with a fixed initial value. Equation 7 is estimated directly to test the null hypothesis of a unit root (i.e. ∅ = 1 if functional form of 0 is known.

But, because we do not know the form of 0 , any testing could be challenging for ∅ = 1 if 0 is incorrectly specified. Thus far, the approach used in this study is based on the assumption that it is possible to approximate 0 using the Fourier expansion: where we don't have a nonlinear trend, all the values of ( 5 = 5 = 0, specification becomes a special case. It is advisable not to use a large value of as this can lead to a problem of overfitting. A number of notable studies such as [START_REF] Bierens | Nonparametric Cointegration Analysis[END_REF], [START_REF] Gallant | On the asymptotic normality of Fourier flexible form estimates[END_REF], and Davies (1987), empirically show the functional form of smooth break using the Fourier approximation. In addition, should be small, as this will allow the evolution of nonlinear trend to be steady. Finally, the resulting equation is shown by the following form: ;

∆ ̂ = ( 3 +
< 5= + ∅ ̂ + ∑ E 5 ∆ ̂ F G F= + H (9)
The dependent variables' lag value in testing the equation is amplified to account for any stationary dynamics in ̂ . Concurrently, the value of the EL test statistic is depicted as 6, in Model A and is used to construct ̂ , 6, when Model B is used, and 6, , when Model C is used. It is vital to know whether a small number of frequency components would duplicate the types of breaks often detected in economic data when the SOR unit root test is used.

To take care of this, we consider a Fourier approximation using a single frequency component represented by k. The amplitude and displacement of the sinusoidal component of the deterministic term is shown by ( 5 and 5 . This enables us to allow for several smooth breaks even with a single frequency I = 1. We can state the hypotheses of unit root testing based on our three models with the Fourier transforms in the subsequent form:

J 3 : L 7 ?? 7 NOP #? 6 O 7? OP& (10)

J : #? 7 NOP $ O 7? OP& Q #? 7 NOP O 0 $ O 7? OP& OP?R 0 67SR O N ?R6 & >ℎO U7 U 6ℎOPV O 0 6S?? ℎ PN 0 W
We test the hypothesis against the critical values using the values for the SOR test for Model A* provided by Shahbaz et al. (2018b).

The Bootstrapping ARDL Bounds Testing Approach to Cointegration

To examine the cointegration relationship between the variables, we use the bootstrapping ARDL cointegration approach of [START_REF] Mcnown | Bootstrapping the autoregressive distributed lag test for cointegration[END_REF]. The originality of the bootstrapping ARDL approach is its ability to deal with weak size and power properties that are faced in the conventional ARDL approach of [START_REF] Pesaran | An autoregressive distributed lag modelling approach to cointegration analysis[END_REF] and [START_REF] Pesaran | Bounds Testing Approaches to the Analysis of Level Relationships[END_REF].

Furthermore, in order to increase the power of T-test and F-test, this approach has the ability to integrate a new cointegration test while drawing on and adding to the conventional ARDL bounds testing approach cointegration framework. [START_REF] Pesaran | Bounds Testing Approaches to the Analysis of Level Relationships[END_REF] entail two conditions for the identification of cointegration. First, the coefficients of error-correction terms must be statistically significant. Second, the coefficients of lagged explanatory variables must also be statistically significant. [START_REF] Pesaran | Bounds Testing Approaches to the Analysis of Level Relationships[END_REF] suggest that one should use the critical bounds (upper and lower bounds) for the second case, but for the first case, there is no bounds test or critical bounds. In the first situation, where coefficients on error-correction terms are statistically significant, the test can be used if all the variables in the model are integrated of order 1.

However, the conventional unit root tests could be awkward due to the low explanatory and power properties they have, as shown by [START_REF] Goh | Bootstrap ARDL on energy-growth relationship for 22 OECD countries[END_REF]. This can be unravelled by using the bootstrapping ARDL bounds test developed by [START_REF] Mcnown | Bootstrapping the autoregressive distributed lag test for cointegration[END_REF].

By not being sensitive to the order of integration properties of the variables and suitable for dynamic time-series models, the bootstrapping ARDL bounds testing approach is unique.

Furthermore, this approach addresses the issue of inconclusive cases, which may arise while using a conventional ARDL bounds testing approach (McNown et al., 2018)6 . One benefit of employing bootstrapping ARDL bounds testing is that the critical values are generated by eliminating the possibility of indecision cases (areas), which occur in traditional bounds testing approach. Furthermore, the bounds testing is useful for dynamic models with more than one explanatory variable. The traditional bootstrapping ARDL bounds testing procedure can be mathematically specified, following [START_REF] Goh | Bootstrap ARDL on energy-growth relationship for 22 OECD countries[END_REF], with three variables as follows:

& = ∑ ( F & F G F= + ∑ X Y X Z X=3 + ∑ * 5 [ 5 \ 5=3 + ∑ , F ,] F= + _ (11)
where i, j, k, and l denote the lags (i = 1, 2… p; j = 0, 1, 2, …, q; k = 0, 1, 2,…r; l = 0, 1, 2,…s;

and t represents time, & is the response variable, Y and [ are the explanatory variables, ,] is a dummy variable that represents the break year based on [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, and * represent the coefficients of the lagged explanatory variables, , is the coefficient of dummy variable. Lastly, _ shows the error-term with zero mean and finite variance. An error correction form of this model can be stated as follows:

∆& = ∅& + *Y + Ψ[ -1 + ∑ λ 7 & -7 V-1 7=1 + ∑ δ c Y -c d-1 c=1 + ∑ π I [ -I P-1 7=1 + ∑ ω 7 , 6 7=1 + _ (12)
In Equation 12,

∅ = ∑ ( F G F= , * = ∑ F Z F=
, and Ψ = ∑ * F \ F=3 . At this point, λ F , δ X , g 5 , and h F account for the associated functions in Equation 1. By transforming a vector auto-regression in the levels into its error-correction form, the derivation of Equation 12from Equation 1 is estimated. Whereas, Equation 12 can be estimated by using a constant term (>) in the unconditional model given below:

∆& = >̃+ ∅ j & + * kY + Ψ j [ + ∑ λ l F & F G F= + ∑ δ l F Y X Z X= + ∑ π k F [ 5 \ 5= + ∑ h m F ,] F= + _ k (

Empirical Analysis and Results Discussion

The results reported in Table 2 show the descriptive statistics and pair-wise correlations. We find The next step is to examine the stationary properties of economic growth, financial development, natural resources, capital, and labour. The investigation of the order of integration of the variables helps us decide which cointegration approach is suitable to examine the cointegration relationship between economic growth and its determinants. The inappropriate order of integration of the variables provides ambiguous empirical results. To overcome these issues, we have applied the ADF unit root test, which accommodates a single unknown structural break in the series [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]. This test is applied to examine if the variables are stationary at level, first difference, or if the variables contain mixed order of integration. This test is appropriate for small sample-size data. Traditional unit root tests, such as ADF [START_REF] Dickey | Likelihood ratio statistics for autoregressive time series with a unit root[END_REF] and PP (Philips and Perron, 1988) tests, over-reject or under-reject the null hypotheses due to their low explanatory power [START_REF] Ng | Lag length selection and the construction of unit root test with good size and power[END_REF]. The ADF unit root test addresses these issues by its superior explanatory power and provides consistent empirical evidence in the presence of structural breaks in the series.

that
Table 3 shows the results of the ADF unit root test with structural break. We find that economic growth, financial development, natural resources, capital and labour contain a unit root problem at level with intercept and trend in the presence of structural breaks. These structural breaks are the outcome of economic reforms, such as the Economic Reform Protection Act 1992, implemented to maintain the macroeconomic performance of Pakistan and hence, economic growth affected. The structural reforms primarily range from 1985 to 2004, which was the main era of economic, financial, and capital reforms in Pakistan. After first difference, we find that all the variables are integrated at I(1) in the presence of structural breaks in the series. In order to test the robustness of the unit root analysis, we have also applied the SOR unit root test, which accounts for the sharp and smooth breaks. The empirical results of SOR unit root test are reported in the lower segment of Table 3. The results noted in Table 3 reveal that the null hypothesis of unit root may not be rejected for financial development and public budget in energy research and development expenditures, as the calculated t-statistics are less than the critical t-values generated by Shahbaz et al. (2018b) 7 . Further, the empirical results show that economic growth, financial development, natural resources, capital, and labour contain unit root processes. The information of nonlinear parameters estimated in Model A* validate the presence of sharp and smooth breaks in the series. 8 The SOR unit root test confirms that at level, all the variables contain unit root problems in the presence of sharp and smooth structural breaks in the series. This implies that all the variables have a unique order of integration i.e. I(1).

This unique order of variable integration leads us to proceed by applying a cointegration approach to examine the presence of cointegration between the variables. In doing so, we have applied the bootstrapping ARDL bounds testing approach developed by [START_REF] Mcnown | Bootstrapping the autoregressive distributed lag test for cointegration[END_REF].

This cointegration test provides empirically reliable results compared to ARDL bounds testing approach developed by [START_REF] Pesaran | Bounds Testing Approaches to the Analysis of Level Relationships[END_REF] Note: *, ** and *** show significance at 1%, 5%, and 10% levels, respectively. We follow AIC to choose optimal lag lengths. F PSS is Fstatistic based on the asymptotic critical bounds, which is generated from the bootstrap method. T DV and T IV are T-statistics for the dependent and independent variables. The LM and JB refer to Lagrange Multiplier test and Jarque-Bera test.

Table 5 deals with a long-run analysis, and we find that financial development has a significant and positive effect on economic growth. Keeping other things constant, a 1% increase in economic growth is accompanied by a 0.4733% improvement in financial development. These findings are similar with [START_REF] Khan | Financial development and economic growth: The case of Pakistan[END_REF], [START_REF] Khan | Trade, financial and growth nexus in Pakistan[END_REF], [START_REF] Jalil | Impact of financial development on economic growth: empirical evidence from Pakistan[END_REF], [START_REF] Shahbaz | Does trade openness affect long run growth? Cointegration, causality and forecast error variance decomposition tests for Pakistan[END_REF][START_REF] Shahbaz | Finance-growth-energy nexus and the role of agriculture and modern sectors: Evidence from ARDL bounds test approach to cointegration in Pakistan[END_REF], who all report that stable financial development spurs economic growth. Furthermore, [START_REF] Polat | Revisiting linkages between financial development, trade openness and economic growth in South Africa: fresh evidence from combined cointegration test[END_REF] argue that financial system development promotes capitalisation, resulting in a boost in economic growth, as their study indicated in Bangladesh. The relationship between natural resources and economic growth is positive and statistically significant, implying that natural resources are blessings rather than a curse. This confirms the presence of the resources-led growth hypothesis. Keeping other things constant, a 1% increase in natural resources abundance increases economic growth by 0.4547%. This confirms the appropriate implementation of resources extraction and usage of resources revenues in productive venture to expedite economic activity and increase economic growth. This empirical evidence is similar with [START_REF] Humphreys | Escaping the Resource Curse[END_REF], [START_REF] Brunnschweiler | Cursing the blessings? Natural resource abundance, institutions, and economic growth[END_REF][START_REF] Mehrara | Reconsidering the resource curse in oil-exporting countries[END_REF], [START_REF] Philippot | Natural Resources and Economic Development in Transition Economies[END_REF], [START_REF] Zagozina | The Resource Curse Paradox: natural resources and economic development in the former Soviet countries[END_REF], [START_REF] Koitsiwe | Relationship between mining revenue, government consumption, exchange rate and economic growth in Botswana[END_REF], [START_REF] Moshiri | Natural Resources, Institutions Quality, and Economic Growth; A Cross-Country Analysis[END_REF], and [START_REF] Arin | The resource curse revisited: A Bayesian model averaging approach[END_REF], who report that natural resources are blessings rather than a curse.

Capital is positively and significantly linked with economic growth dominantly at the 1% level of significance, which identifies the importance of capital in augmented production function. A 0.9151% increase in economic growth is fostered by a 1% increase in capital if all else is same.

This empirical evidence is consistent with [START_REF] Shahbaz | Does trade openness affect long run growth? Cointegration, causality and forecast error variance decomposition tests for Pakistan[END_REF], who reports that capital contributes to economic growth by stimulating economic activity. The relationship between labour and economic growth is statistically significant, which implies that labour, like financial development, natural resources and capital, also plays an important role in expediting economic growth. A 1% increase in labour increases economic growth by 0.3016% by keeping other things constant. The effect of the dummy variable the Economic Reform Protection Act is positive and significant at the 1% level. This reveals that the implementation of the Economic Reform Protection Act (1992), in which investment laws were relaxed to boost investment activities in the country positively affected economic growth.

We include the squared term of financial development into production function to examine if the relationship between financial development and economic growth is inverted-U shaped. The empirical results are reported in Table 5. We note that linear and squared terms of financial development have positive and negative impacts on economic growth, respectively. This reveals that economic growth is positively impacted by financial development initially, but after meeting a threshold level of financial development, economic growth starts declining. Our empirical results indicate the presence of an inverted-U shaped relationship between financial development and economic growth. It shows the importance and proper monitoring of financial resources when allocating productive ventures to stimulate economic growth process. It is beneficial to maintain allocation of financial resources before the threshold point. This relationship is termed as Financial Kuznets curve (FKC). This empirical finding corroborates empirical results reported by Samarghandi et al. (2015) and [START_REF] Moosa | International evidence on the financial Kuznets curve[END_REF], who note that too much finance retards economic growth. On the contrary, [START_REF] Hung | Explaining the nonlinear effects of financial development on economic growth[END_REF] reports the asymmetric relationship between financial development and economic growth. Similarly, the relationship between natural resources and economic growth is inverted-U shaped, which implies that economic growth is initially positive, but it starts to decline after the threshold level of natural resources per capita.

This inverted-U relationship between natural resources and economic growth is consistent with [START_REF] Mehrara | Reconsidering the resource curse in oil-exporting countries[END_REF], who reports that in oil-exporting countries, natural resources exert a positive effect on economic growth, but after the threshold level, natural resources are negatively linked to economic growth. The model has passed all of the diagnostic and stability tests and does not have any issues with normality, serial correlation, autoregressive conditional heteroskedasticity, white heteroskedasticity, and specification. [START_REF] Brown | Techniques for testing the constancy of regression relations over time[END_REF] suggest that the parameter stability can be examined with a CUSUM and CUSUMsq analysis. The CUSUM and CUSUMsq analysis indicates the stability of long-run parameters (Figure 2). The direction of a causal relationship between the variables is tested by applying the VECM Granger causality approach. Table 7 presents these results. In the long run, we find that financial development causes economic growth (validating the supply-side hypothesis), and economic growth causes financial development (confirming the demand-side hypothesis) in the Granger sense. This shows the presence of a feedback effect between financial development and economic growth, which means that both variables are interdependent. These findings are consistent with [START_REF] Shahbaz | Does trade openness affect long run growth? Cointegration, causality and forecast error variance decomposition tests for Pakistan[END_REF]Rahman (2012, 2014a) who reported that financial development and economic growth are complementary. However, [START_REF] Shahbaz | Does trade openness affect long run growth? Cointegration, causality and forecast error variance decomposition tests for Pakistan[END_REF] found that a demand-side effect exists, which reveals that financial development is the cause of economic growth, and Anwer et al. ( 2011) and [START_REF] Ali | Analysis of financial development and economic growth in Pakistan[END_REF] propose that economic growth is the cause of financial development (i.e., the supply-side effect). Similarly, [START_REF] Shaheen | Financial development, international trade and economic growth: Empirical evidence from Pakistan[END_REF] validate the presence of a neutral effect between financial development and economic growth. (i.e., financial development does not Granger-cause economic growth, and economic growth does not Grangercause financial development). A feedback effect exists between natural resources and economic growth, revealing that natural resources cause economic growth, and economic growth Grangercauses natural resources development. We note that natural resources and economic growth are complementary. This empirical finding is consistent with [START_REF] Satti | Empirical evidence on the resource curse hypothesis in oil abundant economy[END_REF] and [START_REF] Ahmed | Dynamics between economic growth, labour, capital and natural resource abundance in Iran: An application of the combined cointegration approach[END_REF], who report the presence of bidirectional causality between natural resources and economic growth in Venezuela and Iran, respectively. Capital causes economic growth, and economic growth Granger-causes increased capital. This empirical result is similar to [START_REF] Satti | Empirical evidence on the resource curse hypothesis in oil abundant economy[END_REF] and [START_REF] Ahmed | Dynamics between economic growth, labour, capital and natural resource abundance in Iran: An application of the combined cointegration approach[END_REF], who find that capital and economic growth are complementary.

A unidirectional causal relationship exists from labour to economic growth and financial development. Natural resources and capital are Granger causes of labour. In the short run, a neutral effect exists between financial development and economic growth (i.e., no causal relationship). Natural resources do not cause economic growth nor does economic growth cause natural resource development. A feedback effect exists between capital and economic growth. Labour causes economic growth, and economic growth causes labour, i.e.

bidirectional causality. The results of long-run and short-run joint causality confirm the short-run and long-run causal relationship between economic growth and its determinants.

Concluding Remarks and Policy Implications

This study investigates the relationship between financial development and economic growth using the production function from 1972-2017. We have incorporated natural resources, capital, and labour as additional determinants of domestic production. The empirical results authenticate the presence of cointegration amid financial development, natural resources, capital, labour, and economic growth. In addition, financial development augments economic growth. The impact of natural resources on economic growth is positive, and the relationship between labour and economic growth is also positive and significant. The implementation of the Economic Reform Protection Act (1992) adds to economic growth. The empirical evidence by a causality test reveals a bidirectional causality between financial development and economic growth. Natural resources lead to economic growth and economic growth Granger-causes natural resources. A feedback effect is found between capital and economic growth. Natural resource abundance Granger-causes labour. A unidirectional causal relationship is found running from labour to economic growth, financial development, natural resources, and capital.

In the context of policy implications, financial development has a positive impact on economic growth. This suggests there should be more financial reforms for achieving sustainable economic development. Our analysis notes that financial development and economic growth are interdependent (i.e., financial system development and economic growth rely on each other). In such a situation, improving a financial system by introducing new financial reforms will have a positive impact on investment activities, which boosts economic growth. The rise in investment activities and income per capita will further increase the demand for financial services, and financial development would be further increased. This suggests an easy monetary implementation, not only for enhancing investment activities, but also for utilizing financial resources to augment domestic production, and hence, economic growth.

In such a situation, natural resources also positively affect economic growth. Natural resource revenue can be used further to boost capitalisation. In doing so, the financial sector can allocate natural resource revenue to productive investment ventures to enhance domestic production.

Capital and labour not only play a direct role in domestic production but also indirectly contribute to economic growth by extracting natural resources. Furthermore, natural resources revenue can be used to improve technical skills of labour by increasing vocational education, which would enhance domestic production, and hence, positively affect economic growth.

  gross fixed capital formation (constant 2010, LCU) and labour (% of total population ages 15-64) and natural resources revenues (constant 2010, LCU) have been collected from the Economic Survey of Pakistan (Government of Pakistan, 2018). For the measuring index of financial development, we use the following sub-indices: broad money (M 2 ) as a share of GDP; domestic credit to private sector as a share of GDP; nonperforming loans as share of total loans (NL); stock market capitalisation as a share of GDP; stock market traded value as a share of GDP; and stock market turnover as a share of GDP. The data for financial indicators is obtained from the Economic Survey of Pakistan(Government of Pakistan, 2018). Before taking the natural logarithm of each variable, all units of the series have been placed into per capita units including financial development sub-indices.

  of cumulative occurrences in the approximation are represented by , k portrays a specific frequency, and the number of observations are shown by A. In this situation,

  13) Equation 13 requires the rejection of the three null hypotheses to confirm the cointegration among the variables & , Y and [ . The hypotheses can be stated as: I) The F 1 test which is based on all the relevant error-correction terms (J 3 : ∅ = * = Ψ = 0 against J : ∅ ≠ * ≠ Ψ ≠ 0 meaning any of ∅, * and Ψ (are) different from zero), II) The F 2 test which is based on all of the explanatory variables terms (J 3 : ∅ = * = 0 against J : * ≠ Ψ ≠ 0 meaning either * and Ψ is different from zero), III) The T-test which is based on the lagged dependent variable (J 3 : ∅ = 0 against J : ∅ ≠ 0 meaning that ∅ is different from zero). A point to note here is that only the critical values of bounds test for F 1 and T-tests are generated in the traditional ARDL approach, yet it ignores the test statistic for F 2 test on the lagged explanatory variables. However, by employing the bootstrapping ARDL approach proposed by McNown et al. (2018), one can provide the critical values for all three tests. Concurrently, to provide empirically vigorous results, we employ the critical values tabulated by McNown et al. (2018).

  financial development is more volatile compared to natural resources. Capitalisation and labour are less volatile compared to economic growth. The results of the Jarque-Bera test reveal that economic growth, financial development, natural resources, capitalisation, and labour have a normal distribution. The empirical analysis of pair-wise correlations indicates a positive correlation between financial development and economic growth. The correlation between natural resources and economic growth is also positive. Capital is positively correlated with economic growth. The correlation between labour and economic growth is positive. Natural resources, capital, and labour are positively linked with financial development. The correlation of natural resources with capital and labour is positive. The negative correlation is found between capital and labour.

  Furthermore, the short-run model has also passed the diagnostic tests. The results show the absence of non-normality in the model. No evidence is found showing a serial correlation in the short-run model. There is the absence of autoregressive conditional heteroskedasticity, and the variance is homoskedastic. The specification of the short-run model is well designed. The results of the CUSUM and CUSUMsq analysis confirm the stability of the short-run parameters at a 5% significance level (Figure-3).

Figure

  Figure-1: CUSUM

Table - 1: Principal Component Analysis and Correlation Matrix Principal Components Analysis

 -

	Number	Value	Difference Proportion Cumulative	Cumulative
					Value	Proportion
	1	3.5057	2.1526	0.5843	3.5057	0.5843
	2	1.3531	0.3908	0.2255	4.8588	0.8098
	3	0.9623	0.8600	0.1604	5.8212	0.9702
	4	0.1022	0.0371	0.0170	5.9234	0.9872
	5	0.0650	0.0536	0.0108	5.9885	0.9981
	6	0.0114	---	0.0019	6.0000	1.0000
	Eigenvectors (Loadings)
	Variable	PC 1	PC 2	PC 3	PC 4	PC 5	PC 6
	0.4328 0.2242 -0.4701 0.2174 0.4893 0.5045 Ordinary Correlations !" !# " $ $	0.4090 -0.6466 0.3297 -0.4540 0.2115 0.2342	-0.2975 -0.4991 0.1956 0.7513 0.1949 0.1465	0.5091 -0.0656 0.0115 0.3128 -0.7696 0.2148	0.1809 0.5210 0.7936 0.1417 0.1779 0.1190	0.5146 -0.0815 -0.0440 0.2531 0.2318 -0.7802
		DC	BM	BNL	MC	ST	STR
	!" !# " $ $	1.0000 0.1274 -0.5772 -0.1171 0.7672 0.8612	1.0000 -0.7252 0.2097 0.1169 0.1245	1.0000 -0.4119 -0.6674 -0.6927	1.0000 0.3617 0.3523	1.0000 0.9425	1.0000

Figure-1: Financial Development Index Source

  : Calculated by AuthorsWe use a Principal Component Analysis (PCA) to generate a financial development index. The

Table 1

 1

, an analysis of a pair-wise correlation is reported. It is noted that broad money (!" ) and domestic credit to private sector (

) are positively correlated. The correlation between domestic credit to private sector ( ) and bank non-performing loans (!# ) is highly negative. Stock market capitalisation (" ) and domestic credit to private sector ( ) are also negatively correlated. Stock market traded value ($ ) is highly and positively correlated with domestic credit to private sector ( ). The correlation is high and positive between stock market turnover ($ ) and domestic credit to private sector ( ). Similarly, a negative correlation exists between broad money and bank nonperforming loans. A negative and high correlation is found between bank nonperforming loans, stock market traded value, and stock market turnover. Stock market traded value and stock market turnover are positively and highly correlated.

  development. The results of the PCA are detailed in the upper portion of Table1. The first principal component explains 58.43% of the variability of an individual variable. The second principal and third components explain 22.55% and 16.04%, respectively, of total variability. The explanation of the total variability by the rest of the principal components is minimal. This suggests to us to first use a PCA as a weight to generate an index of financial development. Financial development improved in Pakistan from 1972 until 2005. After 2005, the development shows a downward trend, which indicates the heavy reliance of government on public debt form the domestic financial sector to meet its budget deficit. This public debt increases the cost of private sector share from domestic credit. Following the "Fiscal Responsibility and Debt Limitation Act 2005," the government needed to maintain public debt below 60% of GDP until June 2013. The government was required to reduce public debt by 2.5% of GDP each year afterwards; however, public debt stood at 63.3% of GDP in December 2013, which further impacted financial development and caused a downward trend in 2014, although it rose after 2015 (Figure

Table - 2: Descriptive Statistics and Pair-wise Correlations

 -

	Variable	T-statistic -2.6263	SOR Sharp-Smooth Structural Break Test A *̅ 3.0916 ( o -0.5665 30.7563 -0.0498 ( 5
		-1.9776	2.0445	-0.8245	-0.0888	0.4974
		-2.4265	4.5654	-0.3268	1.6824	-2.1094
		-3.7914	2.8359	-0.6412	7.7539	-0.3739
		-4.0138	1.3576	-0.3703	-2.5534	0.8716
	Note: * and ** show significance at the 1% and 5% levels, respectively. The optimal lag lengths used are shown in (). ( o , A, *̅ and ( 5 are intercept, trend, slope parameter and threshold parameter, respectively.
		Variable
		Mean	2.6465	1.2668	1.5433	2.2321	1.0034
		Median	2.6586	1.1943	1.5184	2.2380	0.9967
		Maximum	2.7601	1.6727	1.8578	2.2921	1.0259
		Minimum	2.5095	1.0438	0.8257	2.1378	0.9926
		Std. Dev.	0.0707	0.1921	0.1911	0.0383	0.0111
		Skewness	-0.2958	0.7672	-0.5337	-0.7556	0.8503
		Kurtosis	1.9762	2.2200	3.6958	2.9681	2.0699
		Sum	486.9701 233.1074 283.9735 410.7189 184.6263
		Sum Sq.	0.9172	6.7557	6.6844	0.2687	0.0225
		Dev.
			1.0000
			0.2649	1.0000
			0.2941	0.0952	1.0000
			0.3090	0.3217	0.11791	1.0000
			0.3777	0.0853	0.00183	-0.05000	1.0000
			Table-3: Unit Root Analysis
	Variable	ADF at Level with Break	ADF at 1 st Diff. with Break
		T-statistic	P. value	Break Year T-statistic	P. value	Break Year
		-4.6644 (4)	0.1478	1992Q II	-5.5543 (7)**	0.0312	1992Q I
		-4.7962 (5)	0.1235	1995Q I	-6.0814 (6)*	0.0012	2006Q I
		-4.6042 (3)	0.1508	2004Q I	-5.3617 (3)**	0.0415	1999Q I
		-4.7309 (3)	0.1311	1996Q I	-5.6357 (5)**	0.0287	2008Q I
		-3.9397 (6)	0.2457	1985Q I	-5.5867 (3)**	0.0301	1991Q I

Table 4 .

 4 The empirical results of the bootstrapping ARDL bounds testing for cointegration are reported in Our findings from of the F-test and T-test by bootstrapping ARDL may reject the null hypothesis of no cointegration as we treated economic growth, financial development, natural resources, and capital as dependent variables. We fail to reject the null hypothesis as labour is treated as a dependent variable. This indicates that joint F-test and T-test on lagged dependent and t-test on lagged independent variables show the presence of four cointegrating vectors in the framework of Pakistan's domestic production function. We may conclude that economic growth, financial development, natural resources, capital and labour have a long-run relationship during 1972-2017 in Pakistan. The value of R 2 ranges from 0.5808 to 0.7345, which shows that all the variables explain the dependent variables simultaneously. Further, the J-B test confirms the normality of residual terms for all the models.

	Table-4: Bootstrap ARDL Cointegration Analysis
	Bounds Testing Approach to Cointegration Estimated Models Lag Length 2, 2, 2, 2, 1 = s , , , 2, 2, 2, 1, 2 = s , , , 2, 2, 1, 2, 2 = s , , , 2, 2, 2, 1, 2 = s , , , = s , , , 2, 2, 1, 2, 2	Break Year 1992Q II 9 1995Q I 2004Q I 1996Q I 1985Q I	F PSS 24.7326* 11.0616* 5.2463** 13.0975* 2.0218	Diagnostic Tests o q -6 O 0.6463 7.5139 0.6765 5.2041 0.6269 6.4550 -3.7240** 2.9090*** 0.7345 T DV T IV -3.9595* 3.8094* -4.0851* 2.6504** -3.7743** 2.7378** 5.3452 -1.0100 -1.1619 0.5808 1.1101	" 2 0.3529 0.4206 r! 2.2207 1.09 3.0654 0.4242 2.2324 0.1609 2.0270 0.1377

. The bootstrapping ARDL contemplates the joint Ftest and T-test. The F-test and T-test consider lagged values for all level variables and dependent variable, respectively. The T-test (new test) on lagged level of the independent variables is helpful in deciding if cointegration is present between the variables. This shows the superiority of the bootstrapping ARDL over traditional ARDL for examining cointegration between the variables.

Table - 5: Long Run Analysis

 -

	Dependent Variable:
	Variable	Coefficient	t-Statistic	Coefficient	t-Statistic
	Constant	-2.5530	-11.5764	-2.6178	-9.8226
		0.4733*	5.3305	0.8248*	4.6623
		….	….	-0.2818*	-4.3669
		0.4547*	3.3837	0.3212*	3.9378
		….	….	-0.0892*	-3.1434
		0.9151*	18.7897	0.7378*	12.753
		0.3016*	16.615	0.2745*	11.3937
	tt 0c -F-Statistic	0.0384* 0.9336 0.9321 6.2998*	8.6515	-0.0376** 0.9429 0.9410 6.8710*	6.3840
	Stability Analysis
	Test u <v\wx] u ^y\Fx] u z{|} u }y y\v u {yw^x~ CUSUM	F-Statistic 1.0696 1.0714 1.4057 1.2468 1.7424 Stable	p. Value 0.5618 0.4676 0.1206 0.7606 0.1108	F-Statistic 1.1953 0.9078 1.0500 1.5464 1.4210 Stable	p. Value 0.5507 0.5514 0.2215 0.1915 0.1818
	CUSUMsq Stable		Stable

Table 6

 6 reports empirical results of the short run analysis. We find that financial development declines economic growth insignificantly. The relationship between natural resources and economic growth is negative, but it is statistically insignificant. Capital is positive and significantly linked with economic growth at the 1% level of significance. This shows that capital boosts economic activity and hence, economic growth is increased. Labour and economic growth are positively linked at the 1% level of significance, which implies that labour is also an important factor of domestic production that leads economic growth. The dummy variable has a

	positive and significant impact on economic growth; i.e., the Economic Reform Protection Act
	(1992) affects economic growth positively. The estimate of • "	is negative (-0.0345) and
	significant at a 1% level, which corroborates the long-run relationship between financial
	development and economic growth, including other determinants of economic growth. The
	• "	estimate shows that the production function moves to a long-run equilibrium path with a
	3.45% speed of adjustment from the short-run path toward the long-run path. It will take
	approximately 7 years to reach the long-run equilibrium path.
			Table-6: Short Run Analysis
		Dependent Variable:
		Variable	Coefficient	T-statistic	P-value
		Constant	0.0185	11.088	0.0000
			-0.0242	-0.4299	0.6678
			-0.0201	-0.5797	0.5629
			0.0788	3.5233	0.0005
			0.2266	3.2868	0.0012
		t€t • " 0c -F-Statistic	0.0156 -0.0345 0.4903 0.4625 6.8572*	4.4033 3.5901	0.0000 0.0004
		Stability Analysis
		Test u <v\wx] u ^y\Fx] u z{|} u }y y\v u {yw^x~ CUSUM	F-Statistic 1.1606 1.7014 1.4501 1.4862 1.2724 Stable	P. Value 0.5548 0.3076 0.1146 0.5667 0.1218
		CUSUMsq Stable

Table - 7: VECM Granger Causality Analysis

 -

	Variables ∆ ∆ ∆ ∆ ∆	∆ …. 0.1234 [0.8843] 0.1347 [0.8547] 3.0170*** [0.0651] [0.0454] 5.0276**	∆ 1.7819 [0.1869] …. 0.0469 [0.9542] 2.1964 [0.1300] [0.5503] 0.6109	Short Run Causality ∆ 3.6709** 4.5609** ∆ [0.9309] ∆ 0.0718 [0.0382] [0.0420] 0.3600 0.5823 1.8064 [0.7008] [0.5652] [0.1829] …. 1.6461 0.3624 [0.2109] [0.6992] 0.4879 …. 0.1479 [0.6190] [0.8632] [0.1291] [0.8306] 2.2048 0.1868 ….	• " -0.0993** [-2.3370] -0.2325*** [-1.9848] -0.5988* [-2.9851] -0.6331* [-3.3869] [-1.5648] -0.0229	Break Year 1992Q II 1995Q 2004Q 1996Q 1985Q
	∆ ∆ ∆ ∆ ∆	…. 5.5585 [0.0322]** 9.9080 [0.0000] 6.7931 [0.0175]** ….	19.3151 [0.0000]* …. 10.0005 [0.0000]* 9.0104 [0.0009]* ….	Long-and-Short Run Joint Causality 13.3060 11.3168 12.3006 [0.0000]* [0.0000]* [0.0000]* 5.2448 9.5010 5.1852 [0.0285]** [0.0000]* [0.0287]** …. 9.8709 10.0009 [0.0001]* [0.0000]* 10.0017 …. 6.7755 [0.0000]* [0.0176]** …. …. ….	…. …. …. …. ….	1992Q II 1995Q I 2004Q I 1996Q I 1985Q I

Note: * and ** indicate the significance at 1% and 5% levels, respectively.

[START_REF] Shahbaz | Stock market development and economic growth: ARDL causality in Pakistan[END_REF] use stock market capitalisation as a measure of financial development to re-examine the finance-growth nexus. They apply the VECM Granger causality test and find the presence of a feedback effect between the variables, as well as cointegration.

We have transformed all of the variables into real terms and converted them into per capita units by dividing each series by total population.

For more details, seeSricharsoen and Buchenrieder, 2005.

We use the genetic algorithm in our estimation process of the smooth transition trend because it is shown to be the best performing algorithm in estimating LST types of equations. For details, seeOmay and Emirmahmutoglu (2017).

It is well-known that the traditional ARDL bounds testing approach can successfully be applied to empirical models if the variables have mixed order of integration.

Critical T-statistics are found inShahbaz et al. (2018b) from Table5.

For more details (seeShahbaz et al., 2018b)

The critical values of F PSS , T DV and T IV are available upon request.