Gabriel Gomes
email: gabriel.gomes@outlook.fr

Valérie Mignon

Natacha Valla

On the impact of dollar movements on oil currencies

Keywords: C33, F31, Q43 Oil price, Oil currencies, Oil exporting, Non-linearities

This paper investigates to which extent dollar real exchange rate movements have a nonlinear impact on the short term dynamics of the real exchange rate of oil exporting economies. Estimating a panel cointegrating model for 11 OPEC and 5 major oil exporting countries over the 1980-2014 period, we find evidence to support their currencies can be considered as oil price driven. In fact, on the long run a 10% increase in the price of oil leads to a 2.1% appreciation of their real exchange rate. To analyse how dollar movements interact with the real exchange rate of those countries in the short run, we then estimate a panel smooth transition regression model. Results show that the real exchange rate of oil exporting economies is influenced by oil price fluctuations only if the dollar appreciation is lower than 2.6%. After the dollar appreciates beyond this threshold, their currencies are rather affected by other variables.

Introduction

As illustrated by its evolution over the last decade, the price of oil is highly volatile and shocks are poorly forecasted, in particular, because it is hard to split temporary fluctuations from trends. Since the early 2000s until the middle of 2014, oil prices have been showing a sticky upwards trend. Indeed, West Texas Intermediate (WTI) real price has increased around 129% from 2000 to 2010, even with a 38% decrease between 2008 and 2009 due to the financial crisis. [START_REF] Alberola | Internal and external exchange rate equilibrium in a cointegration framework. An application to the Spanish peseta[END_REF] Part of this price boost can be explained by war in Middle East or political instability in Venezuela that created tensions on the supply side, while the BRICS increased their demand for crude oil in order to fuel their fast pace economic growth. Oppositely, after the second semester of 2014 oil prices have embarked on a downward journey, decreasing in real terms by around 6% during 2014 and -45% from June 2014 to December 2014. The reasons for the sharply lower oil prices include increased supply from both traditional and non-traditional sources -such as shale -followed by a lower demand, particularly from high-intensity crude oil consumers such as China. Additionally, the Organization of Petroleum Exporting Countries (OPEC), and Saudi Arabia in particular, seems to have changed their willingness to play the role of swing producer -lowering production in response to declining prices -which in the past stabilized oil price cliffs.

In oil exporting countries this kind of price instability is very costly. Their economies and budgets adjust asymmetrically to a positive or negative shock on the price of oil. First, in the absence of counter-cyclical measures, oil price fluctuations spill over the real exchange rate. The latter tends to appreciate after an oil price boom (Habib and Kalamova, 2007; Korhonen and Juurikala, 2007; Dauvin, 2014), consequently, declining non-oil exports. [START_REF] Amano | Exchange Rate and Oil Prices[END_REF] Currencies with such pattern -a real exchange rate that co-moves with the price of oil -are called 'oil currencies' (Chen and Rogoff, 2003). Nevertheless, in these economies the real exchange rate appreciation may turn into a problem when it is caused by a momentary oil price increase. After the price of oil gets back its 'pre-boom level', the windfall revenues subsequently fall and the real exchange rate automatically becomes overvalued, hence harming permanently other economical sectors such as agriculture and manufacturing.

Moreover, in most of these countries oil rents are paid directly to the government. Thus, a positive shock in the oil price is often followed by an increase in public spending and a decrease in its efficiency. An unexpected drop in prices tend to cut oil rent bonanzas and most part of the inefficient public spending becomes no longer sustainable. In a long term perspective, continuous boosts in oil prices have been associated with current account imbalances across the globe. As a matter of fact, during the last decade oil exporting countries have emerged as the ones with the largest current account surplus. This has induced renewed interest in the economies of these countries and in their exchange rates' dynamics, through which global imbalances can be adjusted.

Furthermore, due to network externalities or to optimize transaction costs, some oil exporting economies tend to manage their nominal exchange rates either by anchoring their currencies to a basket or to a single 'strong' currency such as the dollar or the euro (Coudert et al., 2012). Yet, long and short-term relationships between the dollar and the oil price have been evidenced in different works (Armano and van Norden, 1995; Coudert et al., 2008). Such relationship suggests that even float economies with oil currencies should have their exchange rates in some extent affected by dollar movements, hence throwing spotlight on a connection between dollar movements and oil exporting countries' real exchange rates, which we aim to explore in this paper.

As a matter of fact, swings in the dollar real exchange rate have different effects on oil exporting and importing countries. A weak dollar increases the purchasing power in oil importing economies (except the USA), although negatively affecting oil exporting countries. Conversely, an overvalued dollar may adversely affect oil importing countries leading to a demand shock in the long term that ultimately affects oil exporting countries (Killian, 2009; Reboredo, 2012). It is therefore essential to determine beyond (until) which appreciation rate the dollar can be considered to be overvalued (undervalued) in order to establish how dollar movements interact with the real exchange rate of oil exporting countries' currencies.

To this end, considering a sample of 16 oil exporting countries over the 1980-2014 period, we estimate a panel smooth transition regression (PSTR) model to describe the dynamics of their real exchange rate changes. The PSTR is a nonlinear specification that groups the observations in different regimes according to the value of a chosen (transition) variable, in our case, the dollar real exchange rate variations. The PSTR estimation shows that the short term dynamics of oil exporting countries' real exchange rate is affected by dollar fluctuations. In fact, we find that shocks in the price of oil have a positive significant impact on oil economies' real exchange rate only if the dollar real exchange rate appreciation is lower than 2.6%. Nevertheless, after the dollar appreciation exceeds this threshold value, we find no evidence for the existence of oil currencies in the short run. Furthermore, our results are robust regardless of the exchange rate arrangement, which is important since not all oil exporting economies are likely to peg their currencies against the dollar.

Our work contributes to the existing literature on oil currencies in two ways. First, it uses suitable econometric techniques that take into account the cross-sectional dependence between oil exporting economies, allowing us to find a robust long-term specification for their real exchange rate. Second, by exploring the interaction between dollar fluctuations and the drivers of oil economies' exchange rate, we offer a deeper understanding of oil currencies. This paper is structured as follows. Section 2 reviews the existing literature, and presents the theoretical framework tested in our empirical analysis. Section 3 describes the data and offers some graphical insights. Section 4 performs all the compulsory econometric tests, estimates the cointegrating relationship and the error-correction model. Section 5 introduces and estimates the PSTR model. Section 6 concludes.

Oil currencies

Theoretical overview

Theoretically, the connection between the price of oil and the real exchange rate has been explored in different frameworks. We can identify a first set of studies developed by Krugman (1983) and Golub (1983) that focus on the links between the US dollar and oil price from a somewhat wealth transfer perspective. Krugman (1983) considers the world is divided in three areas: the United States, Germany and OPEC. The first two sell goods and services to each other while OPEC only sells oil at a price that is assumed exogenously fixed in dollars. There are only two assets in this model: dollars and marks; OPEC allocates their wealth between the two. The author uses a dynamic partial equilibrium model that allows distinguishing short and long run impacts, and thus between financial and real approaches, respectively. In the short run, the effect of an oil price increase depends on the US share in the global oil imports compared to the percentage of dollar assets held by OPEC. If American oil imports have a higher share than the dollar assets in OPEC's portfolio, then an increase in the price of oil will depreciate the dollar exchange rate. In the long run, the impact is determined by the weight of oil in the US total imports with regards to the share of OPEC total imports from the US. If US trade balance with OPEC is positive, then an oil price increase would benefit OPEC imports from the US, which would depreciate the dollar exchange rate in the long term. Golub (1983) also assumes that the world is divided in three areas: OPEC, the US and the European Union. The model considers the US dollar and the Deutsch mark as the only two financial assets and studies the effects of oil price shocks on wealth transfer and consequently on portfolio equilibrium. The impact oil price movements have on exchange rates depends on the way wealth reallocation increases or decreases the overall demand for dollars. If OPEC has a preference for holding dollars, an oil price increase is followed by a dollar appreciation as the extra revenues would be allocated in dollar assets.

In this paper, we rely on another set of models that leans on a terms of trade perspective. [START_REF] Backus | Oil prices and the terms of trade[END_REF] More precisely, we consider the theoretical framework developed by Cashin et al. (2004) that will be formally introduced in the next subsection. This model finds its inspiration in the works of Neary (1988), De Gregory and Wolf (1994) and Obstfeld and Rogoff (1996), among others, that use quasi-similar frameworks where the economy is composed of two sectors: one producing a tradable good, and the other producing nontradable good. Labor is supposed to move freely and nominal wages are the same across sectors. In this context, better terms of trade leads to an increase in wages in the tradable goods sector, which translates in higher non-traded goods price, thus appreciating the domestic currency in real terms. Baxter and Kouparitsas (2000) explore the sources of terms of trade volatility considering two main components: a 'goods-price effect' and a 'country-price effect'. The former refers to a country that exports one basket of goods, imports a different basket and has its terms of trade fluctuations driven by international changes in the price of the goods it exports. The 'country-price effect' concerns fluctuations of a country's terms of trade that arise from different selling prices of a same good in different countries. They find that for fuel products exporters most of the terms of trade variation come from goods-price effects. This evidences the key role of the price of petroleum in these countries' terms of trade. [START_REF] Baffes | Oil spills on other commodities[END_REF]

Theoretical model

The theoretical model developed by Cashin, Céspedes and Sahay (2004) determines the real exchange rate of a commodity exporting country to be a function of its terms of trade and the productivity differential between its non-traded sector and traded sectors of its trading partners. The model considers a small open economy composed of two different sectors: the one associated with the production of a primary commodity is the tradable good sector, and the other producing a non-exporting good is the non-traded sector. In our case, the tradable good is crude oil, which is traded with the rest of the world against manufactured goods. Firms in both sectors are supposed to benefit from constant returns to scale technology in a perfect competition structure. Labor is the only input factor for producing traded and non-traded goods and can move freely across sectors. Finally, households supply labor inelastically and increase their utility by consuming the domestic -non-traded -good and the -imported -foreign good.

Domestic firms

As aforementioned, the domestic economy has two sectors: one producing a tradable, X, good such as crude oil; and another that makes a non-tradable, N , good. Firms have similar technology and thus identical profit maximization problems:

max L i Π i = P i Y i -wL i s.t. Y i = η i L i
where i can take the notation of either the non-tradable sector, N , or the tradable sector, X; L X (L N ) is the labor demanded in the oil (non-traded) sector. Firms in both sectors maximize their revenues, P i Y i , discounting labor costs, wL i , subject to a technology constraint, η i L i , where P i stands for the price of the good, Y i the quantity produced, w are nominal wages which are equalized across sectors due to free labor movement and η i the labor marginal productivity that varies across sectors. At the equilibrium, the marginal productivity must equal the real wage in each sector i:

η i = w P i (2.1)
Crude oil is only consumed abroad while non-tradable goods are only consumed in the domestic country. Hence, the price of oil (P X ) is determined exogenously by the world's demand and supply, whereas non-tradable goods' price (P N ) is determined by domestic's demand and supply. Nominal wages are the same across sectors, this implies:

P N η N = P X η X = w (2.2)
P N = η X η N P X Equation (2.
2) gives us a first insight about how oil prices affect domestic prices. The only determinant of the relative price of non-traded goods in terms of oil ( P N/P X ) is the productivity differential between the tradable and non-tradable sectors( η N/η X ). Hence, a positive shock in the price of oil -and consequent increase in the wage the exportable sector -translates into a rise in the wage and price of the non-tradable sector.

Domestic households

Domestic households consume the domestic produced non-traded good (N ) along with an imported good (T ) that has its price in domestic currency determined by the nominal exchange rate (E) assuming the law of one price as follows:

P T = P * T E (2.3)
where P * T is the price of the imported good in the foreign country's currency, E is expressed as the number of foreign currency units per domestic currency unit.

Households are assumed to be identical and maximize their utility over the consumption (C) of the non-traded domestic good and the imported traded good (T ), subject to the expenditure of their total wealth (w). Households supply labor inelastically (L = L N + L X ). The maximization program of a representative household is characterized by a Cobb-Douglas function as follows:

max C N ,C T U = (C N ) α (C T ) 1-α s.t. w = P N C N + P T C T where α ∈]0; 1[.
Solving the maximization problem above we find the consumer price index (CPI) of the oil exporting country, which is a weighted average of the consumed goods price:

P = (P N ) γ (P T ) 1-γ (2.4)

Foreign firms

The foreign economy is composed of three sectors: non-traded (N * ) and traded (T * ) sectors, and a third producing an intermediate good (I * ). The only input for producing the non-traded and the intermediate good is labor, and the sectors productivity are respectively, η * N and η * I . As for the domestic economy, the non-traded good is only consumed in the foreign country, and nominal wages are equal across sectors which leads to:

P * N = η * I η * N P * I (2.5)
The firms producing the final good need two inputs in the manufacturing process. The first is crude oil (Y * X ) imported from several countries among which figures our domestic economy. The second is the intermediate good (Y * I ) . The profit maximization problem of a representative firm is hence:

max Y * X ,Y * I Π T = P * T Y * T -(P * X Y * X + P * I Y * I ) s.t. Y * T = λ(Y * I ) β (Y * X ) 1-β
where λ is a constant. The solution to the maximization problem yields the cost of a unit of the tradable good, expressed in terms of the foreign currency, as a geometric average of the intermediate good and oil good prices:

P * T = (P * I ) β (P * T ) 1-β (2.6)

Foreign households

Foreign households consume their non-traded good (N * ) along with the final good produced in the foreign country (T * ), considering a same labor market structure as in the domestic economy, consumer price index in the foreign economy can be written as:

P * = (P * N ) γ (P * T ) 1-γ (2.7)

Real exchange rate determination

We define the real exchange rate (RER) of the oil exporting economy as the ratio between the foreign price of the domestic consumption basket (EP ) and the foreign price of the foreign consumption basket (P * ) as follows:

RER = EP P * (2.8)
where an increase in E means that the domestic currency appreciates.

Plugging Equations (2.4) and (2.7) in Equation (2.8) and then using Equations (2.2), (2.3) and (2.5) we can determine the real exchange rate of the domestic country as a function of the productivity differential and the terms of trade:

RER = ( η X η * I η * N η N )( P * X P * I ) γ (2.9)
The two productivity differentials can be interpreted as the Balassa-Samuelson effect, thus implying that the domestic currency appreciates in real terms if domestic productivity in the tradable sector exceeds productivity in nontradable sector relatively to the trading partners. The last ratio -the relative price of crude oil exports in terms of imports expressed in the foreign country currency -denotes the terms of trade of the oil exporting country.

Empirical overview

Empirically, the relationship between terms of trade and the real exchange rate has been tested and proved for many energy and non-energy commodity exporting countries. Here, we will focus on the literature related to oil exporters. Overall, empirical studies on the dynamics of the real exchange rate explained by the price of oil can be classified in two categories: 1) studies relying on time-series data offering a country-by-country analysis; 2) studies using panel data features allowing to deal with limited sample sizes.

The country-by-country approach

A country-by-country analysis enables to estimate the real exchange rate for each country separately, hence considering the specificities of each individual economy.

In this context, Spatafora and Stavrev (2003) Mehara and Oskoui (2007) rely on a SVAR approach to study the sources of macroeconomic fluctuations over 1970-2002 in four oil exporting countries: Iran, Indonesia, Kuwait and Saudi Arabia. [START_REF] Baxter | What causes fluctuations in the terms of trade?[END_REF] Their results show that in Indonesia and Saudi Arabia an oil price boom leads to an appreciation of the real exchange rate in the long run, whereas in Iran and Kuwait the real exchange rate tends to depreciate. Authors suggest this unexpected effect may be associated with the Kuwait's structural reforms and 'oil revenue fund', while in Iran this might be due to exchange restrictions and import controls.

Nevertheless, the aforementioned literature and results should be interpreted carefully. As a matter of fact, the majority of these studies deal with short length series due to scarce data and political breaks (i.e. Russia). A time series approach may, therefore, offer distorted results.

The panel data approach

More recent and sophisticated studies have investigated the oil currency -oil price nexus from a panel data perspective in order to overcome the problem of short time series. However, only Dauvin (2014) considers the cross-dependence between energy exporting countries, using appropriate econometric techniques in her study. Due to inaccurate econometric methodology, previous studies on oil currencies tend to over-find cointegration between the real exchange rate of oil exporting economies and other variables, thus obtaining spurious results of the impact that oil price fluctuations have on the real effective exchange rate of oil exporting countries.

Data and stylized facts

This section introduces our empirical study by discussing about possible control variables that could be incorporated to our model, then it presents the econometric framework, the data used and the preliminary econometric analysis.

Exchange-rate determinants

Apart from terms of trade and productivity differentials, a wide range of variables has been used for explaining exchange rates. [START_REF] Cashin | Commodity currencies and the real exchange rate[END_REF] In this subsection we briefly discuss whether it is possible to include additional variables in our theoretical framework in order to obtain a more robust empirical analysis.

First, as a consequence of the increasingly capital mobility across borders, indicators reflecting the international economic environment such as the real interest rate differentials may be relevant to exchange rates' short term dynamics. Nevertheless, historical data for real interest rates is pretty much scarce for oil exporting economies and when available is often subject to measurement problems.

Second, variables related to the fiscal stance and monetary policy considerations are often included as determinants of the real exchange rate, because they reflect the domestic economic environment. In this context, government spending relative to GDP is one variable typically included in such regressions. In the short to medium run, if private sector lowers its demand for goods less than the increase in government spending, or if the marginal propensity of the public sector to spend on non-traded good is higher than the one from the private sector, a positive impact of government spending on the real exchange rate can be conceived. For oil exporting countries the latter possibility appears plausible since in these countries government sector's spending on infrastructure, for instance, is mainly satisfied by domestic inputs. Therefore, a rise in government spending could positively affect the real exchange rate via higher demand for non-traded goods. In the long term, however, higher government spending, in particular if kept unbalanced, may lead to economic distortions undermining the market's confidence in the domestic currency. On the financial sector side, two variables can be identified in the literature: money balances and credit growth. For oil economies, though, such variables are incomplete or nonexistent.

Moreover, on a macroeconomic balance perspective, the foreign indebtedness of a country is often assumed to be an important exchange rate fundamental (Alberola et al., 1999). This latter can be captured by a country's net foreign asset position (nf a), which is available for oil exporting economies with few limitations that can be overcome using accumulated current account positions. The impact of the net foreign asset position of an oil exporting economy on its exchange rate depends on the share of foreign assets that compose the portfolio of local agents. If they tend to hold domestic (foreign) assets, a better nf a would have a positive (negative) impact on the real exchange rate as the domestic (foreign) currency would be more requested.

Finally, some studies suggest that variations on the degree of a country's economic openness should influence its exchange rate dynamics. [START_REF] Rogoff | Commodity currencies[END_REF] Likely to the government spending, there are arguments in favor of a positive or a negative impact of the economic openness on the real exchange rate. On the one hand, the more open to trade a country is, the less it relies on protection and distortions to its external accounts: hence increasing the openness should enhance the country's economic performance and lead to an appreciation of the real exchange rate. On the other hand, greater openness to foreign trade may lead to a real depreciation if lower tariffs on imports or taxes on exports are applied (Edwards, 1994).

Relying on this background and also on data availability, three variables are added to Cashin et al. ( 2004)'s theoretical model, namely: government spending (gov), the degree of economic openness (open) and the net foreign asset position (nf a).

Sample

We consider yearly data from 1980 to 2014 for 16 countries among which 11 are OPEC members and 5 are major oil exporting countries. The OPEC members are: Algeria, Angola, Ecuador, Iran, Kuwait, Libya, Nigeria, Qatar, Saudi Arabia, United Arab Emirates and Venezuela. The other countries are: Bahrain, Congo, Gabon, Norway and Oman. Over 2000-2010 they were responsible for on average 60% of the world's total crude oil exports.9

In the countries included in our analysis, crude oil accounts for a large part of the total exports, and its rents represent an important share of the GDP. Additionally, their terms of trade (T oT ) and the real price of oil (roil) they export are highly correlated. Table 1 illustrates the importance of oil exports to their economies. [START_REF] Choudhri | Real exchange rates in developing countries: are Balassa-Samuelson effects present?[END_REF] Table 1: Selected oil exporting countries

Data

The real effective exchange rate (reer) series are extracted from Bruegel's database. [START_REF] Coudert | Does euro or dollar pegging impact the real exchange rate? The Case of Oil and Commodity Currencies[END_REF] The reer of a given country is calculated as a weighted average between its nominal bilateral exchange rate and that from its trading partners, adjusted to its price movements relative to that of its trading partners. An increase in the reer means an appreciation of it.

The real price of oil (roil) is the annual average of the spot oil price in dollars used as benchmark by an exporting country deflated by the sticky American CPI from St. Louis Fed database. [START_REF] Coudert | Pegging emerging currencies in the face of dollar swings[END_REF] For Bahrain and Oman, we use the price of Dubai retrieved from the IFS database. [START_REF] Coudert | Oil price and the dollar[END_REF] For Norway, Congo and Gabon the oil price is the one from Brent, which is provided by the EIA. [START_REF] Darvas | Real effective exchange rates for 178 countries: a new database[END_REF] Finally, for OPEC countries the oil price used is the one from the OPEC basket and comes from OPEC database. [START_REF] Dauvin | Energy prices and the real exchange rate of commodity-exporting countries[END_REF] The productivity differential or Balassa-Samuelson effect (bs) is proxied by the PPP GDP of the concerned country relative to its trading partners. We construct the weights for each trading partner following the same countries used in Bruegel's reer calculations, allowing for a consistent measure of our variable. PPP GDP and GDP data are extracted from IFS database.

The control variablesgov, open and nf a -were extracted from World Bank World Development Indicators and updated for 2014 using IMF FM report. [START_REF] De Gregorio | Terms of trade, productivity and the real exchange rate[END_REF] General government expenditure, gov, includes all government current expenditures for purchases of goods and services (including compensation of employees). It also includes most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. The degree of economic openness, open, is proxied by the sum of exports and imports of goods and services measured as a share of gross domestic product. Finally, the net foreign asset position, nf a, is the sum of foreign assets held by monetary authorities and deposit money banks, less their foreign liabilities, expressed in percentage points of GDP.

Graphical evidence

Figure 1 gives a first insight of the connections between the real effective exchange rate and our chosen variables for Libya, Kuwait, Ecuador and Norway. [START_REF] Golub | Oil prices and exchange rates[END_REF] We selected these countries based on the information provided by Table 1 in order to have a general (non exhaustive) illustration of our variables' evolution. The first two, Libya and Kuwait, have strongly oil dependent economies, whereas the other two, Ecuador and Norway, have more diversified economies. Overall, the graphs confirm our assumptions about the link between the variables. We observe that the real effective exchange rate tends to co-move with the real price of oil, the productivity differential and the government spending variables; whilst moving in opposite direction to the degree of economic openness. In fact, in Figure 1, the only exception is Libya's real oil price that seems disconnected from its real exchange rate. Previous studies on oil currencies may have obtained spurious results on the impact oil price fluctuations have over the exchange rate of oil exporting countries. As a matter of fact, as we previously discussed, only Dauvin (2014) -who does not focus her work on oil exporting countries -seems to have considered the cross-section dependence between energy exporting economies.

Panel unit root and cointegration tests

Cross-sectional dependence in oil exporting economies

As a first step, we must determine if our variables are correlated across countries in order to choose the appropriate set of panel unit root tests to be performed in the next subsection. In our case, this test is meaningful since OPEC, which composes half of our panel, is believed to influence the oil market at a world level.

In this context, we perform the error cross-sectional dependence test (CD) proposed by Pesaran (2004). This test is based on an average of pairwise correlation coefficients of OLS residuals from individual regressions and is robust to single and multiple structural breaks in the slope coefficients and the error variances of individual regressions. Results are displayed in Table 2 and indicate that, except for nf a, the null hypothesis of cross-section independence is strongly rejected for all time series. Moreover, the correlation coefficients are rather high.

Panel unit root tests

In the presence of cross-section dependence, the so-called first generation panel unit root tests -that rely on the cross-section independence assumption -tend to reject the null hypothesis of unit root excessively. [START_REF] González | Panel smooth transition models[END_REF] Therefore we perform two second generation unit root tests: Choi (2002) and Pesaran (2007). The former test relies on an error-component panel model and eliminates the cross-section dependence by removing both individual and time trend effects. The second test is based on the mean of individual ADF tstatistics, eliminating the cross-section dependence by a single factor model that augments the ADF regression with the lagged cross-sectional mean and its first difference of the individual series (CADS statistics). [START_REF] Habib | Are there oil currencies?[END_REF] Table 3 summarizes both test results which leads us to conclude that all series but nf a are I(1).

Table 3: Panel unit root tests results

Panel cointegration tests

As a third step, we apply panel cointegration tests that reveal whether there is a linear combination of our series with time invariant properties, meaning they follow a common stochastic process. Here we perform Westerlund (2007) panel cointegration test, which has the null hypothesis of no cointegration, to all the I(1) variables, thus excluding nf a. The test offers two main advantages: 1) it allows for a large degree of heterogeneity, both in the long run relationship and in the short run dynamics of the error correction model; 2) it is designed to handle cross-sectional dependent data through bootstrapping of the test statistics. Results are shown in Table 4 and confirm that all series are cointegrated. 20

The cointegrating relationship

We draw an empirical model based on Cashin et al. ( 2004) and on the literature on exchange rate determinants. Thus, our econometric framework assumes that, in the long term, the real effective exchange rate (reer) of an oil exporting country is driven by: the real price of oil (roil) it exports, its tradable sector relative productivity (bs), its government spending (gov) and its economy degree of openness (open), the last two variables being expressed in terms of GDP percentage.

The long-term specification is therefore:

l.reer it = µ i + γ 1 l.roil it + γ 2 l.bs it + α 1 gov it + α 2 open it + ε it (4.1)
where x it is a variable for country i on year t with l.x it = ln(x it ); µ i accounts for individual effects and ε it is the error term.

Our series being I(1) and cointegrated, we begin by estimating Equation (4.1). To this end, we rely on the Panel Dynamic OLS (DOLS) procedure proposed by Kao and Chiang (2000) and Mark and Sul (2003). This procedure involves a parametric adjustment to the errors of the static regression, which is achieved by assuming that there is a relationship between the [START_REF] Hansen | Threshold effects in non-dynamic panels: estimation, testing, and inference[END_REF] We select the optimal number of leads and lags in order to minimize the Akaike's Information Criterion. We run the tests with constant and trend, no constant or trend, and with constant but no trend. We also consider the robust p-values obtained after bootstrapping using 250 replicates in order to avoid misleading inference in case of crossmember correlation.

residuals from the static regression and first differences of leads, lags and contemporaneous values of the explanatory variables in first differences. [START_REF] Hurlin | Une synthèse des tests de racine unitaire sur données de panel[END_REF] The estimated cointegrating relationship with the estimators p-values in parentheses is given by: l.reer i,t = μi + 0.21 (0.00) l.roil i,t + 0.14 (0.03) l.bs i,t + 0.33

(0.07) gov i,t -0.74 (0.00) open i,t (4.2)
As expected, except for the economic openness all variables have a positive impact of the real effective exchange rate. The real price of oil is significant at the 1% level, supposing that the real exchange rate should appreciate by roughly 2.1% after a 10% increase in the price of oil. The productivity differential variable is significant at the 5% level and its coefficient is not very different from that of the real price of oil, which confirms our theoretical model intuition. On the long term, the real exchange rate seems to be also influenced by the government spending and the economic openness. The coefficient of the first variable is significant at the 10% level and has a positive impact on the real exchange rate, which confirms our intuition that in oil exporting countries the marginal propensity of the public sector to spend on non-traded good is higher that the one from the private sector. The last variable is significant at the 1% level and according to its coefficient the real exchange rate should depreciate by around 7.4% after a 10% increase of the economic openness. As we discussed in subsection 3.1, this negative elasticity is coherent with oil exporting economies because they usually import most of the goods consumed domestically.

Estimation of the error-correction model

The existence of a cointegrating relationship between our variables allows us to estimate an error-correction model (ECM). To this end, we calculate the error-correction term as the difference between the observed real effective exchange rate and its equilibrium value given by the cointegrating relationship, lagged from one period: z i,t-1 = l.reer i,t-1 -l.reer i,t-1 . Results are the following with the estimators p-values in parentheses:

21 OLS estimates of Equation (4.1) have biased distributions that depend on the nuisance parameter corresponding to the serial correlation properties of the data. We also estimate Equation (4.1) using the Fully-Modified OLS (FMOLS) procedure proposed by Phillips and Hansen (1990). Results are very similar, even though, this last may suffer from higher size distortion when compared to the DOLS method.

∆l.reer i,t = -0.16

(0.00) z i,
∆open i,t (4.3)
As expected, we find a negative and significant coefficient for the errorcorrection term, implying a mean reverting process. More precisely, if the estimated real exchange rate in the previous period is lower (higher) than its observed value, then the real exchange rate will decrease (increase) in the current period, adjusting 16% of its misalignment every year. Moreover, the only variable that seems to have a short run impact on the real exchange rate change is the economic openness. As our theoretical model establishes, the real price of oil and the productivity differential have an impact over the real exchange rate through the wage channel, which is not as flexible in the short run as it is in the long run. The same applies to a rise in government spending, which translates into higher demand for non traded goods, thus impacting the real exchange rate in the long run. Finally, the degree of economic openness, as discussed in subsection 3.1, affects the real exchange rate through the imports channel, which is likely to be variant in the short run.

Investigating non-linearities in the short term dynamics

Early studies related to the price of oil have focused on its connection with the dollar. Armano and van Norden (1996) in a pioneer empirical work study the relationship between the two variables from February 1972 to December 1985, concluding that a 10% increase in the real oil price appreciates the dollar real exchange rate by 5.13%. Coudert et al. (2008) cover the period January 1980 -November 2004 and also find that both variables are cointegrated, estimating that a 10% rise in the price of oil has a positive impact of 4.2% on the dollar real exchange rate.

In a more recent study, Brémond et al. (2014) show that after 1989, dollar movements have a negative and non constant impact on the price of Brent. In fact, from 1989 to 2013 they observe a succession of elasticity's cycles of the real exchange rate of the dollar on the price of oil: from 1989 to 1997 (from 0.0 to -0.8), from 1997 to mid-2003 (from -0.8 to -0.2), from mid-2003 to June 2008 (from -0.2 to -1.2) and from 2008 to 2013 (from -1.2 to -0.45).

Besides this time-varying impact, swings in the dollar exchange rate have different effects on oil exporting and importing countries. Oil importing economies benefit from a weak dollar while oil exporting countries lose part of their purchasing power. Oppositely, an overvalued dollar has a dual effect for oil exporting economies. On the one hand, it increases the purchasing power of oil exporting counties. On the other hand, it may lead to a demand shock in the oil market thus harming their economies if its value becomes too high. However, this nonlinear behavior that dollar fluctuations may have on the real exchange rate of oil exporting countries remains uncovered.

Nevertheless, if dollar movements indeed influence the elasticity of oil economies' real exchange rate with regards to their drivers, and more precisely, to the price of oil, these economies should consider it, in order to stabilize their real exchange rates.

The PSTR framework

The Panel Smooth Transition Regression (PSTR) model, presented by González and Teräsvirta (2005), is a fixed effects model with exogenous regressors, which is useful for describing heterogenous panels with time-varying coefficients across individuals. It consists in grouping the observations in different regimes (usually 2) according to the value of a transition variable. The framework is a generalization of the threshold panel model developed by Hansen (1999), and its main feature is that the transition from one regime to another may occur smoothly.

The basic PSTR model with two extreme regimes can be written as follows:

y i,t = µ i + β ′ 1 x i,t + β ′ 2 x i,t g(s i,t ; γ, c) + u i,t (5.1)
where the transition function, g(s i,t ; γ, c) is a continuous function that is normalized to be bounded between 0 and 1; s i,t is the transition variable that triggers the shift from one regime to another when it reaches a certain threshold value, c; the speed of adjustment from one regime to another is determined by γ.

The transition function for a two regimes PSTR model is:

g(s i,t ; γ, c) = [1 + exp (-γ (s i,t -c))] -1 (5.2)
with γ > 0. The described model implies that the two regimes are associated with low and high values values of s i,t .

Furthermore, González and Teräsvirta (2005) propose a three-steps strategy for estimating PSTR models. In the first step, which concerns the specification of the model, we test for the null hypothesis of linearity using the LM-test statistic provided by the authors along with Fisher and LRT statistics. This step is also important for selecting the most relevant transition variable and the number of regimes. [START_REF] Kao | On the estimation and inference of a cointegrated regression in panel data[END_REF] The second step estimates the model with nonlinear least squares (NLS), after individual effects µ i are eliminated by removing individual-specific means. Finally, in the third step, we evaluate the validity of the model by applying misspecification tests.

We start by specifying our error-correction model in a non-linear form as follows:

∆l.reer it = µ i + (θ 1 z i,t-1 + β ′ 1 X i,t )+ (θ 2 z i,t-1 + β ′ 2 X i,t )g(q i,t ; γ, c)+ ε i,t (5.3)
Where X i,t represents the vector of real exchange rate determinants in first difference, namely, ∆l.roil i,t , ∆l.bs i,t , ∆gov i,t , ∆open i,t , and β is a vector of coefficients associated to each one of the variables according to the regime, which is indexed, respectively, by 1 or 2. Depending on the value of the transition variable, the link between ∆l.reer i,t and its drivers switches from θ 1 and β 1 in Regime 1 to θ 2 and β 2 in Regime 2; with θ 1 + θ 2 and β 1 + β 2 the total coefficients value after the threshold value is exceeded.

PSTR estimation results

We apply linearity tests considering two transition variables: the real effective exchange rate of the dollar retrieved from the same database as our countries'; the dollar index, which is a measure of the value of the U.S. dollar relative to its most significant trading partners. [START_REF] Killian | Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market[END_REF] For both variables test results strongly reject the null hypothesis of linearity. [START_REF] Koranchelian | The equilibrium real exchange rate in a commodity exporting country: Algeria's experience[END_REF] Table 5 shows the PSTR estimation results. Regardless of the transition variable chosen, we observe that the transition occurs slowly, the estimated [START_REF] Kao | On the estimation and inference of a cointegrated regression in panel data[END_REF] From an empirical perspective, it is sufficient to capture the presence of non-linearities to first consider the existence of a maximum of 3 regimes and then test for the existence of 2 regimes. [START_REF] Killian | Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market[END_REF] Currently, this index is calculated by factoring in the exchange rates of six major world currencies: the euro, Japanese yen, Canadian dollar, British pound, Swedish krona and Swiss franc. Data was retrieved from St Louis Fed database and we set 2010 = 100. [START_REF] Koranchelian | The equilibrium real exchange rate in a commodity exporting country: Algeria's experience[END_REF] The p-value associated to LM, Fischer and LRT statistics is equal to 0.00 for both transition variables.

coefficients and the threshold have close values. Regime 1 coincides with a depreciating or slightly appreciating dollar, whereas Regime 2 corresponds to an appreciating dollar. First, let us analyze the estimated coefficients in Regime 1. Opposite from Equation (4.3), except for bs, their coefficients are statistically significant, whereas, their signs are in line with our cointegrating relationship from Equation (4.2). The explanation for this is rather intuitive, considering a weak dollar. A rise in the price of oil is unlikely to affect the demand of crude oil because importers have greater purchasing power. Consequently, an increase in the real price of oil is followed by an appreciation of the real exchange rate of oil exporters. Economic agents tend to have higher expectations about future crude oil exports and hence government spending tends to have a positive impact on the real exchange rate. Finally, imports are likely to increase in oil exporting countries with floating currencies and therefore positive variations in their economic openness depreciate their real exchange rate.

Then, many interesting facts emerge when we consider the coefficients after the variation of the dollar real effective exchange rate reaches its threshold. As a matter of fact, if we add Regime 1 and Regime 2 values our results become much closer to those from the estimation of Equation (4.3). The impact of the real oil price variations over the real exchange rate changes becomes close to zero. The government spending switches from 1.09 to -1.12, while the economic openness and the error correction term are not affected and stay significant and negative. [START_REF] Korhonnen | Equilibrium exchange rates in oil-dependent countries[END_REF] A possible explanation for our results follows. With a strong dollar, variations in the price of oil are likely to increase the chances of a demand shock in the long term, as oil importers start to lose purchasing power, thus having a negative impact on the real exchange rate. Overall, the impact of the real price of oil on the real exchange rate becomes close to zero because wages, that are the channel through which shocks in the price of oil are transmitted to the real exchange rate, are unlikely to change in the short run. Conversely, oil exporting economies increase their purchasing power, leading them to loosen fiscal constraints which reduces the efficiency of government spending. Ergo, the latter starts to have a negative impact on the real exchange rate of these economies. 26 25 The matching between values above the threshold and the explaining variables we described also fits the dollar index variations. As a matter of fact, around 92% of the observations for the fluctuations of real exchange rate of the dollar that are higher than 0.026 are also higher than 0.031. [START_REF] Krugman | Oil and the Dollar[END_REF] We note, by grid searching, that over the study period the dollar real exchange rate variations are above the estimated threshold value 13 times, of which around 92% of points match with a decrease in the real price of oil, 85% correspond to a negative variation of During the studied period, roughly 63% of the observations belong to Regime 1, meaning that in the short run the real exchange rate of our 16 oil exporting countries is often affected by fluctuations in the real price of oil.

Does the exchange rate arrangement matter?

It is important to notice that half of our countries have (or have had) been pegged to the USD. By definition, in countries pegged to the dollar, their real exchange rate is likely to co-move with the dollar itself rather than with other macroeconomic variables. If this is the case here, our previous results capture the monetary authorities response to an appreciation of the anchor currency rather than the impact of our drivers on the real exchange rate, which explains why all coefficients are negative in Regime 2. [START_REF] Macdonald | Concepts to calculate equilibrium exchange rates: an overview[END_REF] In order to determine the accuracy of our analysis in subsection 5.2, we estimate a PSTR model similar to the one described above including only countries that have not been de facto pegged to the dollar in the last 20 years. [START_REF] Mark | Cointegration vector estimation by panel DOLS and long-run money demand[END_REF] We apply all the previous econometric steps described in section 4, but for the sake of space, we present only the PSTR estimation results (Table 6). [START_REF] Mehara | The sources of macroeconomic fluctuations in oil exporting countries: A comparative study[END_REF] productivity variable, 77% coincide with a positive variation in the government spending, whereas no matching can be made with the economic openness variable. [START_REF] Macdonald | Concepts to calculate equilibrium exchange rates: an overview[END_REF] If monetary authorities anticipate an appreciation of the pegged currency, they are likely to operate in the foreign exchange market in order to depreciate their currency. [START_REF] Mark | Cointegration vector estimation by panel DOLS and long-run money demand[END_REF] The not USD pegged counties are: Algeria, Angola, Congo, Gabon, Kuwait, Nigeria, Norway, Libya. [START_REF] Mehara | The sources of macroeconomic fluctuations in oil exporting countries: A comparative study[END_REF] Results can be provided upon request to the author.

Table 6: PSTR results not USD pegged

We can observe that the transition occurs roughly, which is not surprising since we purged the exchange rate movement inertia implied by the dollar peg. As in our first PSTR estimation, the estimated coefficients and the threshold have close values regardless of the transition variable chosen. Overall, the estimated coefficients have similar signs and close intensity to that of the first PSTR.

These similar results in both PSTR estimations show that our first estimation does not suffer from the endogeneity problem discussed above, meaning that dollar movements interact with oil economies' real exchange rate whether they are USD pegged or not. Nevertheless, it is important to notice that the threshold values are lower and close to zero in this second estimation. This supposes that the transition occurs whenever the dollar appreciates, which seems normal for not USD pegged countries. In fact, their economies adjust through the nominal exchange rate which is more volatile than the real exchange rate, thus implying a lower threshold. Furthermore, we observe that around 52% of the observations belong to Regime 1, meaning that not USD pegged countries are less likely to have oil currencies in the short run.

Conclusion

This paper investigated the impact of the US dollar movements on the link between the real oil price and real exchange rates of oil exporting countries.

To this end, we consider a sample of 16 oil exporters over the 1980-2014 period. We estimate a long term cointegrating relationship, finding evidence to support the existence of oil currencies in the long run. Our cointegrating relationship shows that a 10% increase in the price of oil leads these countries' currency to appreciate by roughly 2.1% in real terms.

Studying the influence of the dollar over the short run dynamics of real exchange rate changes, we find that the US currency exercises a non-linear impact on oil currencies' real exchange rate. For our whole sample of oil exporters, the price of oil affects their real exchange rate only if the dollar appreciation does not exceed a 2.6% threshold. For not USD pegged countries this value is equal to zero, meaning that whenever the dollar appreciates in real terms their currencies are no longer affected by the real price of oil.

On the whole, in the short run, the real exchange rate of not USD pegged oil exporting countries is less often affected by fluctuations in the real price of oil than USD pegged countries.

Figure 1 :

 1 Figure 1: Oil economies' real exchange rate

Table 2 :

 2 Pesaran (2004) CD test results

Table 4 :

 4 Westerlund (2007) cointegration test results

Table 5 :

 5 PSTR results

Nominal WTI price deflated by the US sticky CPI (2010 = 100) retrieved from St. Louis Fed database.

This is often taken as the main symptom of the so-called Dutch disease. However, the decline in non-oil exports is not in and of itself a cause of reduced welfare promoted by the real exchange rate appreciaton.

Terms of trade are defined as the price of a country's exports relative to the price of its imports.

They consider 16 fuel exporters: Algeria, Congo, Ecuador, Egypt, Gabon, Indonesia, Kuwait, Mexico, Nigeria, Norway, Saudi Arabia, Syria, Trinidad and Tobago, Tunisia, United Arab Emirates and Venezuela; in a total of 100 countries.

For Kuwait and Saudi Arabia the covered periods are, respectively,1972-2002 and 1971-2002.

The 10 energy exporting economies are: Australia, Colombia, South Africa, Algeria, Iran, Nigeria, Saudi Arabia, Venezuela, Canada and Norway.

See MacDonald (2000) for a detailed overview.

See Kemme et al. (2000) and Kim and Korhonen (2002), for instance.

According to the EIA statistics.

Five other countries fit our criteria: Iraq, Syria, Russia, Azerbaijan and Kazakhstan; but could not be included for the following reasons. Most of the variables used in our study did not exist for Iraq. Syria has been going through political instabilities since 2010. Russia, Azerbaijan and Kazakhstan have reliable data only after 1995, as most of the econometric features we use rely on lags and leads, thus depending on series with at least 30 years of observations, we prefer not to include them.

Available at: http://www.bruegel.org/datasets/; we use the reer calculations based on 67 trading partners.

Available at: http://research.stlouisfed.org/fred2/series/CPILFESL#

Available at: http://www.opendataforafrica.org/IMFPCP2014Jan

Available at: http://www.eia.gov/dnav/pet/pet_pri_spt_s1_d.htm

Available at: http://www.opec.org/library/Annual%20Statistical%20Bulletin/ Prior to 1983 we use the price of brent as benchmark. From 1993 to 2007 we use the WTI as Ecuador's oil price because during this period the country was not an OPEC member. For Gabon from 1980 to 1994 we use the same benchmark price as OPEC, since the country was also a member of the organization.

For the United Arab Emirates(1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998),Qatar (1985Qatar ( -1989) ) andLibya (1985Libya ( -1989) ) we completed missing points in gov and open series using, respectively, investment data's trend and autoregressive models. We also acknowledge that we completed our series up to 2014 based on the figures from IMF October 2014 report version that was not the 2014 final. Nevertheless, we have enough evidence from past years to believe values that appear on the report should go through minimal changes until the final version for 2014. Moreover, nf a series were completed using current account data's trend provided by the World Bank.

Graphs for other countries can be provided upon request to the author.

See Hurlin and Mignon (2006) and Hurlin (2010) for a detailed review of panel unit root tests.

The lag length considerably influences the test results. We determine the optimal number of lags by two approaches: AIC criterion and Newey and West's (1994) plug-in procedure.