Crude Oil Price and Retail Price of Gasoline: An Empirical Analysis of Asymmetric Relationship Tugrul GURGUR and Zubeyir KILINC* Abstract The literature provides a set of heterogeneous results regarding a possible asymmetric relationship between the cost of crude oil and retail price of gasoline. In this study, we provide empirical evidence on the relationship in Turkey. Following the literature, we disentangle the crude price in foreign currency and exchange rate. Using a partial decomposition approach and estimating the determinants of retail prices via an NARDL model we show that the impacts of crude oil price and exchange rate on the retail price of gasoline differ in a significant way. In particular, the transmission of an exchange rate shock to the retail price is more rapid and more significant in magnitude compared to that of a shock to crude price in foreign currency. Our results also show that the source of asymmetric relationship is exchange rate. The response of retail price to depreciation in domestic currency is more rapid and larger in magnitude compared to that of appreciation. The response of retail price to the movements in crude price in foreign currency is symmetric. Finally, the estimation results reveal the fact that moderate changes in neither exchange rate nor international price of crude oil are reflected to the retail prices. JEL Classification: C22; C51; Q40. Keywords: Asymmetric Relationship, Error Correction Models, Exchange Rate, Gasoline Prices, Retail Price, Nonlinear Autoregressive Distributed Lag (NARDL), Threshold Analysis, Turkey.
* GURGUR: Central Bank of the Republic of Turkey,
[email protected] ▪ KILINC: Central Bank of the Republic of Turkey,
[email protected] ▪ The views expressed in the paper are those of the author and do not represent those of the Central Bank of the Republic of Turkey, or its staff.
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1. Introduction The potential asymmetry between the prices of crude oil and gasoline has always been an issue for all agents in the economy including the policymakers as well as households. The interest intensifies especially in times of high volatility in oil market as public opinion complains about perceived disconnect between the movements in crude oil price and in the retail prices of petroleum products. A widely shared belief among general public is that, the rise in input costs is fully passed to the retail prices with no delay, whereas declines appear only partially at the pump and even then with long delays. In other words, “retail prices rise like a rocket, but falls like a feather” (Bacon, 1991). There is a long literature that examines whether the retail price of gasoline asymmetrically responds to the decreases and increases in the cost of crude oil; however, it does not provide an unequivocal answer. Perdiguero-Garcia (2013), in his well-designed meta-analysis, argues that there are various reasons behind this heterogeneity among empirical studies ranging from the estimation method to the time frequency of data. Therefore, from an econometric point of view, it is necessary to clarify the channels through which crude oil price may affect retail price and enrich the empirical literature with additional country-specific analysis. In examining the nature of pricing in gasoline market, it is common to express the cost of crude oil in domestic currency, which implies that the pass-through from crude price (in foreign currency) and exchange rate are equal. This practice of embedding the impact of crude price and exchange rate might be problematic especially in a small open economy, where both profit margin and the other costs are functions of exchange rate due to high dollarization. When domestic currency depreciates; not only the relative prices change, but also the general price level shifts upwards, which raises both the costs of energy companies, e.g. through higher wages, rents, etc., and possibly profit margins. Exchange rate would act as a proxy for such shifts. Hence, the adjustment to retail prices from a change in crude price may not be the same as that from a change in exchange rate. In the literature there are a number of examples that disentangle foreign currency price of crude oil and exchange rate (for example Asplund et al., 2000; Polemis, 2012; and Bagnai and Ospina, 2015). These studies report that separating two channels is crucial to correctly capture the pricing dynamics in the gasoline market, since the effects of crude price and exchange rate
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might differ both in the long-run and short-run. Moreover, the separation might provide more insights about the asymmetric relationship. In this study, we investigate the relationship between the cost of crude oil and retail price of gasoline in Turkey. Using a partial decomposition approach we disentangle the cost passthrough to retail price into two distinct channels: movements in international price of crude oil and movements in exchange rate. This type of specification not only enriches the analysis, but also controls for biasedness in the estimates that may emerge due to convolution of movements in crude price and exchange rate. Then, we estimate the determinants of retail price via a nonlinear autoregressive distributed lag (NARDL) model that enables the quantification of asymmetry both in the long-run (cointegrating) relation and short-run (error-correction) relation. Finally, we address the presence of strategic pricing, where pass-through to retail prices not only depends on the sign (i.e. positive versus negative), but also the size of shocks in the explanatory variables. In particular, we consider a three-tier pricing policy that includes two regions (positive and negative) for large swings in costs and an inaction band in between. Our contribution to the literature is two folds: First, we distinguish between crude price and exchange rate channels by decomposing the domestic price of crude oil as partial sums in a nonlinear dynamic framework, rather than simply using crude price and exchange rate as two distinct explanatory variables. This formulation enables us to measure the sources of changes in the domestic price of crude oil and attribute these changes separately to movements in crude oil price (in US dollar) and to movements in exchange rate. Second, we consider the asymmetry in pricing not only in the short-run relation, but also in the long-run. Existing studies usually address the former and focus on asymmetry in the adjustment parameters. However, short-run asymmetry may also be carried out to a long-run relation. If that is the case, then the estimates for the error correction term would be biased. Our results show that crude oil price shocks and exchange rate shocks have distinct consequences over retail price of gasoline. In particular, transmission of latter shocks is more rapid and more significant in magnitude than the former shock. Hence, the practice of embedding the impact of crude price and exchange rate would yield biased results. We also find evidence of asymmetry in pricing in the case of exchange rate. Currency depreciations are reflected to retail prices not only more rapidly than currency appreciations, but also with a larger pass-through. Changes in international price of crude oil, on the other hand, affect retail prices in a symmetric 3
manner, both in terms of magnitude and duration of adjustment. Lastly, the estimation results also reveal the existence of an inactive band in pricing strategy, where moderate changes in exchange rate or international price of crude oil are not reflected to retail prices. Next section provides a brief summary of the literature. Section 3 discusses the dynamics in the Turkish petroleum market. The data is presented in Section 4, followed by the econometric model in Section 5 and the estimation method in Section 6. The results and implications are presented in Section 7. Section 8 investigates the robustness of the results to alternative model specifications. Finally, Section 9 concludes the paper.
2. Literature Review Asymmetry is generally defined as strategic pricing by oil companies where retail prices respond differently to crude oil price increases and decreases. However, as the review of the literature by Frey and Manera (2007) points out, the word “asymmetry” does not have a unique meaning as researchers have addressed different types of asymmetries using a wide range of methods. To that end, they introduce eight different asymmetry concepts, which can be grouped under three categories: differences in short-run impact parameters, differences in error correction term and differences in functional forms, i.e. regimes. A common feature of these definitions is that they all refer to asymmetries in the adjustment process. Such asymmetries have been investigated thoroughly in the literature (Borenstein et al., 1997; Asplund et al., 2000; Godby et al., 2000; Bachmeier and Griffin, 2003; Galeotti et al., 2003; Chen et al., 2005; Kaufmann and Laskowski, 2005; Douglas, 2010; Liu et al., 2010; Romano and Scandurra, 2012; Balaguer and Ripollés, 2012; Valadkhani, 2013). In recent years, inspired by the hidden cointegration concept introduced by Granger and Yoon (2002) and Schorderet (2003) a more comprehensive approach has emerged, where the presence of asymmetry is studied not only in the adjustment parameters but also in the cointegrating relation.1 A distinctive feature of asymmetric cointegration is that a stationary relationship exists between nonstationary components of data series, rather than the series themselves. The major advantage of this approach is that asymmetry is analyzed simultaneously in the adjustment parameters and long-run (cointegrating) relation. The studies on long-run asymmetry are rather limited and more recent (Atil et al., 2014; Bagnai and Ospina, 2015). 1
The hidden cointegration represents a particular case of asymmetric cointegration, where common dynamics of time series is observed in their positive and/or negative components.
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The type of asymmetry studies by researchers also dictates the choice of appropriate econometric method. Empirical ones on short-run asymmetry usually relies on asymmetric error correction model (Granger and Lee, 1989) or threshold error correction model (Hansen, 2000). Long-run asymmetry, on the other hand, is studied by nonlinear autoregressive distributed lag (NARDL) approach proposed by Shin et al. (2014). NARDL models allow for asymmetries in both the short- and long-run parameters (for a more detailed review of econometric models used in asymmetric price transmission, see Grasso and Manera, 2007 and Hassouneh et al., 2012). The findings of the empirical studies on asymmetry in gasoline prices are quite diverse and can be divided into three groups. The first group claims that it is hard to find an asymmetric relationship between crude price and retail price of gasoline. Godby et al. (2000) for Canada, Bachmeier and Griffin (2003) for the United Kingdom, Liu et al. (2010) for New Zealand, and Balaguer and Ripollés (2012) for Spain find almost no evidence of price asymmetry in the gasoline market. The second group of studies claims that adjustment in the price of gasoline is asymmetric to decreases and increases in crude price. While Borenstein et al. (1997) observe an asymmetric relationship in different segments of the US petroleum market, Galeotti et al. (2003) report that price of gasoline asymmetrically responds to the decreases and increases in crude price within a number of European economies. Angelopoulou and Gibson (2010) and Polemis (2012) find report similar results in Greece. Valadkhani (2013), one of the very few disaggregated studies in the literature, find evidence of location-specific asymmetric pricing in Australia. As mentioned above, the studies on long-run asymmetry are rather limited and more recent. Atil et al. (2014) and Bagnai and Ospina (2015) use a NARDL approach that enables testing for asymmetry both in cointegrating relation and short-run adjustment and report the existence of asymmetry in not only the short-run, but also in the long-run. They argue that ignoring asymmetry in the latter would compromise the reliability of the long- and short-run parameters estimates. Finally, the third group asserts that the asymmetric relationship is conditional on some other determinants. Douglas (2010) shows that the asymmetric response of price of gasoline in the US is mainly driven by a small number of outlying observations that are far below or above the equilibrium price level. Romano and Scandurra (2012) suggest that the asymmetric relationship is observable in the periods of low price volatility where the asymmetry almost disappears otherwise. Chen et al. (2005) find that that the observed asymmetry in price 5
transmission primarily occurs downstream – not upstream – of the transmission process. Kaufmann and Laskowski (2005) find that the asymmetric relation between the price of crude oil and gasoline is generated by refinery utilization rates and inventory behavior. Polemis and Fotis (2014) show that less competitive gasoline markets (such as Greece, Spain, Netherlands, Portugal and Ireland) exhibit price asymmetry, while highly competitive gasoline markets (such as Germany and the United Kingdom) follow a symmetric price adjustment path. Perdiguero-Garcia (2013) claims that heterogeneity in the literature can be due to a number of reasons such as methodology, model employed, frequency of the data, time period considered, country of interest, particular segment of the market analyzed in the study and even the type of publication. In particular, he finds that it is less likely to spot asymmetry when more aggregated data is used due to temporal aggregation problem that influenced the data generating process. It is more likely to observe asymmetry in downstream, i.e. retail market, as opposed to upstream, the wholesale segment, since the former is typically more concentrated and less competitive. It is also found that type of fuel (e.g. diesel vs. gasoline) does not influence the likelihood of asymmetry since companies often establish common pricing strategies for all fuels sold. Furthermore, Godby et al. (2000), Bachmeier and Griffin (2003), and Balaguer and Ripollés (2012) cite the frequency of data as a reason for conflicting results and caution for using low frequency data in empirical analysis. Liu et al. (2010), on the other hand, attribute the lack of asymmetry to the competitive nature of petroleum market in New Zealand. As for the reasons behind the asymmetric adjustment, the literature identifies a number of possible channels. First, non-competitive market structure of the economy may lead the asymmetry (Honarvar, 2009). Second, Tappata (2009) suggests that consumers’ imperfect information and cost of search may drive the asymmetric response of gasoline price. Third, as claimed by Kaufmann and Laskowski (2005) and Balke et al. (2002), inventory management and refining adjustment costs may also explain the asymmetric behavior. Another related line of research, which is more relevant for small open economies, shows that it is essential to distinguish between price of crude oil in foreign currency and exchange rate while analyzing the relationship between the cost of crude oil and retail price of gasoline. Asplund et al. (2000), for instance, show that retail price of gasoline responds more rapidly to exchange rate movements than to the crude price movements in Sweden. Galeotti et al. (2003) show that exchange rate plays a very relevant role in determining the retail price of gasoline in 6
European petroleum market. Angelopoulou and Gibson (2010) and Polemis (2012) find that exchange rate might play a significant role in explaining the pass-through from crude price to the price of gasoline in Greece. Moreover, they report that the asymmetry might primarily due to exchange rate. Bagnai and Ospina (2015) claim that it is essential to separate these two; otherwise, the empirical results might be biased towards accepting the symmetric behavior particularly in the long-run.2 A shortcoming of the aforementioned studies is that they all use international price of crude oil and exchange rate as two distinct variables to estimate their individual impacts on retail prices. This type of modeling necessitates a logarithmic transformation of underlying variables, which assumes a proportional cost margin, i.e. the crude-retail margin increases with the price of crude oil. As pointed out by Borenstein et al. (1997), this assumption is not usually supported by the data. Also, a log-linear specification may be less appropriate in a developing country with moderate to high inflation since the margin would likely to increase steadily in response to the increase in general price level. Regarding the Turkish petroleum market, there are two recent studies that investigate the relationship between the cost of crude oil and the retail price of gasoline. Akcelik and Ozmen (2014) use a static modeling approach on daily data to predict the pass-through, where each point of observation is a pricing spell. Thus, variables in the model correspond to the cumulative changes during each spell. They find that pass-through to retail prices is higher in the case of a positive shock to crude oil price as compared to the case of a negative shock. Moreover, retail price increases more if the source of positive cost shock is currency depreciation rather than the increase in international prices. Our work differs from Akcelik and Ozmen (2014) in several fronts, but most importantly we consider a dynamic model that allows us to identify cost passthrough not only in magnitude but also in duration and timing. Another study on Turkey is Berument et al. (2014) who use an asymmetric error correction model to estimate the relative effects of crude oil price and exchange rate in Northern Mediterranean countries, including Turkey. The authors look at the asymmetry only in the short-run, but do not find any in the case of Turkey. One reason for the lack of asymmetry may be the time frequency of data, which is weekly. In contrast, we use daily data that is free from temporal aggregation problem.
2
For a more detailed discussion on the relevance of distinction between crude price and exchange rate see Bagnai and Ospina (2015) and the references therein.
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3. Petroleum Market in Turkey The oil market has been regulated by the Petroleum Market Law, which was enacted in 2003 and has been put in effect on the first day of 2005. Following the enactment of the Law, Turkey had abandoned the automatic pricing mechanism in the petroleum market and moved to free market pricing. In a nutshell, the law ruled that the price of petroleum would be constituted according to the nearest accessible global free market conditions. Regarding the domestic crude price, the free market price that is formed in the nearest delivery port or refinery would be accepted as the price. In particular, while forming the prices in the oil market, the price of CIF MED (Genova/Lavera) published by Platts European Market Scan is taken as the base (EMRA, 2014). The Law orders the distributors to submit their own ceiling prices along with their vendors’ ceiling prices to Energy Market Regulatory Authority (EMRA), which checks the compatibility of the prices with the world price. It is also allowed to issue regulations that are necessary to let the market operate freely. As Figure 1 shows the price formation in the market can be divided into three stages: refinery price, distributor price and final price. Price of Brent oil acts as the base price in European crude oil market, whereas pricing in petroleum market reflects crude oil and refinement, as well as transportation, insurance and other related costs. Benchmark prices of a variety of petroleum products at the spot and futures market in Europe are published by the Platts, a source of price assessments in the physical energy markets. For distributors that import petroleum products, Platts quotations represent the approximate cost of importing; however, Turkey has the refinery capacity as well. There are four refineries operating in Turkey, which are under the control of one company, TUPRAS. For refineries, Platts quotations act as reference points. Oil companies suggest the final price of gasoline daily by adding a retail margin to the wholesale price, which reflects refinery and distributor margin. Every gasoline station manager can charge their own prices as well. Although TUPRAS has a potential for dominating the market for final products, the competition in the market is strengthened by the fact that the distributors can also import final products of petroleum. In 2014, for instance, the final product import of the distributors was around 12 million tons, which is more than half of the final product import of refineries and is almost equal to the amount that the distributors bought from the refineries.
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As of 2014, there are 73 licensed distributors in the country where the market share of the largest five companies is around 75 percent.3 Regarding the distribution of final products sold in the country around 85 percent of the final petroleum products sold within 2014 were diesel gas where around 10 percent was unleaded gas. Final sales price, denoted by 𝑃, is determined according to the following formula: 𝑃 = (𝐶𝑅𝐷 + 𝑅𝑒𝑓𝑖𝑛𝑒𝑟𝑦 𝑠ℎ𝑎𝑟𝑒 + 𝐼𝑛𝑐𝑜𝑚𝑒 𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝐸𝑀𝑅𝐴 + 𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑜𝑟 𝑠ℎ𝑎𝑟𝑒 + 𝑅𝑒𝑡𝑎𝑖𝑙𝑒𝑟 𝑠ℎ𝑎𝑟𝑒 + 𝑠𝑐𝑡) ∗ (1 + 𝑣𝑎𝑡)
(1)
𝑠𝑐𝑡 stands for special consumption tax, which is a fixed amount added to ex-refinery prices and
paid in lump sum. It varies among different types of petroleum products. 𝑣𝑎𝑡 represents valueadded tax that is added to final retail price. It is 18 percent for all products. The combined share of taxes is around 64 percent of retail price of gasoline as of 2015. Stripping the final price from taxes, one can calculate the net price. Spot price of refined products represents around 72 percent of the net price in 2015, whereas gross margin of retailer, distributors and refineries represents the remaining portion (EMRA, 2015). The Law requires that the pricing of petroleum products would be constituted according to the nearest accessible global free market conditions. In other words, the free market price that is formed in the nearest delivery port or refinery would be used as the reference of domestic prices. In practice, while forming the prices of petroleum products, price of CIF MED is used by EMRA. The Law authorizes EMRA to check the compatibility of domestic prices with reference to international prices and intervenes to the market once it observes that pricing behavior is not in line with competitive market conditions. In particular, EMRA is allowed to determine base and/or ceiling price(s) and take necessary measures to apply on regional or national basis in all phases of activities not exceeding two months at each time, in the case that risks arising from agreements and activities aimed at or may result in hindering, disrupting or restricting competitive environment and delivery in the petroleum market. Although EMRA is the most important regulator of the petroleum market, Turkish Competition Authority (TCA) is also authorized by the Act on Protection of Competition, which aims the purpose of prohibiting agreements, decisions and practices preventing, distorting or restricting competition in the markets for all goods and services. This is particularly important because EMRA utilized its authority to intervene the market for the first time on June 28 th 2009, 3
These companies are OMV Petrol Ofisi, OPET, SHELL&TURCAS, BP and TOTAL Oil.
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following an investigation by TCA. TCA, in its investigation, did not find any strong evidence for further investigation; however, it remarked some structural problems that hamper the competition in the market and recommended EMRA to intervene the market. Then, EMRA determined a ceiling price by indexing the prices to CIF MED oil price and fixed the margins for distributors and vendors. This intervention resulted in an instant and significant decrease in the product prices. After five years of no intervention period, EMRA determined ceiling prices in the petroleum market in two more instances, on March 21st 2014 and on February 21st 2015. In both interventions, EMRA decreased the margins of distributors and vendors after determining that the domestic price was not constituted according to the nearest accessible global free market conditions.
4. Data As mentioned above a big bulk of final petroleum products sold in Turkey is diesel gas; however, the retail price of gasoline is more closely followed. This might be due to the relatively high number of households consuming gasoline compared to diesel gas. Although making the analysis for both diesel gas and unleaded gas is unavoidable to get the full picture of the market, in this study, we only report the results for 95-octane unleaded gasoline.4 Since Perdiguero-Garcia (2013) claims that the most disaggregated data is more suitable to better understand the relationship between the cost of crude oil and retail price of, we use daily data in our analysis. Moreover, his meta-analysis suggests that the probability of finding an asymmetry in the retail segment is relatively high; therefore, we examine whether there exists an asymmetric relation at this segment of the market. Besides, due to the fact that we distinguish between crude price and exchange rate channels, this segment is probably the most affected one compared to the other segments of the system. We collect average, pre-tax retail price of gasoline in Istanbul, Ankara and Izmir, and the corresponding international wholesale price of refined fuel oil. The data for retailer price, obtained from EMRA and energy companies, are in domestic currency. Exchange rate for Turkish Lira (TL) per US dollar is gathered from the Central Bank of Turkey. Finally, crude price is in US dollar and collected from the Energy Information Administration of the United States.
4
We also replicated the analysis for diesel gas and the main results are very similar to those for gasoline.
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Although the available sample period is from January 1st 2005 to December 31st 2015, to exclude the distortionary effects of transition to the new pricing regime on pricing dynamics, we start our examination from January 2006. There is an observation for every day of the week; however, the retail price is almost unchanged on Sundays and Mondays. This reflects the pricing policy of the companies in Turkey. In case there is a change in the retail prices, the decision is announced during the weekdays and becomes effective in the next business day. All series are tested for the presence of unit roots using Augmented Dickey-Fuller (ADF), the Phillips-Perron (PP), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. Lag selection is based on the modified Akaike information criterion (AIC). Following Elder and Kennedy (2001), for variables that exhibit a natural pattern of growth (such as exchange rate) we also test the presence of unit root with drift versus trend stationarity. For variables that exhibit no growth in the long-run, we test the presence of unit root with no drift versus mean stationarity. The results are presented in Table 1. The table shows that all series display unit root. We also test for unit roots under structural breaks in the series, since it is well established that the existence of structural breaks may cause biasedness towards non-rejection of unit root (Perron, 1989). We use the test proposed by Zivot and Andrews (1992) and Perron (1997). The former test assumes unit root in the null hypothesis versus (trend) stationary series with one endogenous break in the alternative hypothesis. Perron test, on the other hand, assumes one endogenous structural break in the null hypothesis. Following Sen (2003) we use a changing intercept model for variables that exhibit no natural pattern of growth and both a changing trend and a mixed model for variables that exhibit natural pattern of growth. The results for the unit root tests with structural breaks are reported in Table 2. Overall, we do not find any evidence that structural breaks play any role in the data generating processes, i.e. the series have unit roots whether or not there exist any structural breaks.
5. Model Specification In this study, we use an approach similar to Asplund et al. (2000), which decomposes the change in crude oil price in domestic currency in two parts: changes due to exchange rate holding crude price in foreign currency fixed and changes due to crude price in foreign currency holding exchange rate fixed. However, our method is more generalized, since we extend this idea to the estimation of cointegrating relation keeping the data generating process of the series intact.
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The reduced-form static representation of price of gasoline can be written as a linear function of crude oil price as follows: P𝑡 = α0 + α1 CRD𝑡 + ϵ𝑡
(2)
where P𝑡 denotes retail price of gasoline and CRD𝑡 represents crude oil price in domestic currency at time t. Here, α1 is the cost pass-through and α0 represents the gross margin, which is constant due to the linear specification.5 One drawback of equation (2) is that pass-through from exchange rate and crude oil price (in foreign currency) are assumed to be conceptually identical and empirically equal. An alternative model specification, adding exchange rate as a separate regressor may correct the potential bias, but interpretation of the coefficients would be problematic since exchange rate would influence retail price indirectly as well. The estimates are also likely to be statistically insignificant due to high collinearity between the regressors. Another option is using US dollar value of crude oil price and exchange rate as two separate regressors, but that would yield biased estimates since the impact of the former is proportional to the value of the latter and vice versa. We modify these equations to be able to obtain unbiased estimates of the parameters. Let CRFt be the crude oil price in foreign currency and ER 𝑡 be exchange rate (TL per US dollar): CRDt = CRFt ∗ ER t
(3)
Then, the change in crude oil price in local currency, ∆CRDt can be written as: ∆CRDt = CRFt ∗ ER t − CRFt−1 ∗ ER t−1
(4)
By adding and subtracting CRFt ∗ ER t−1 from both sides, we can decompose ∆CRDt into two parts based on the source of change in the cost:6 ∆CRDt = ER t−1 ∗ ∆CRFt + CRFt ∗ ∆ER t
(5)
While the first part on the right hand side represents a change due to movements in US dollar value of crude oil price, the second part denotes a change due to the movements in exchange rate. Next, we create two time series from these two components: 𝑡
𝐶𝑅𝐷𝑡∆𝐶𝑅𝐹 = ∑ 𝐸𝑅𝑖−1 ∗ ∆𝐶𝑅𝐹𝑖
(6)
𝑖=1 5
Since, it is not realistic to expect gross margin to stay unchanged over a long period, it is customary to include a linear time trend in the regression as an approximation of the price index change. During the periods of high inflation, a quadratic term may also be necessary. For simplicity, we do not write the time trend in the equations, but use and report them in our empirical analysis. 6 Alternatively by adding and subtracting 𝐶𝑅𝐹𝑡−1 ∗ 𝐸𝑅𝑡 from both sides one can also write ∆𝐶𝑅𝐷𝑡 as 𝐸𝑅𝑡 ∗ ∆𝐶𝑅𝐹𝑡 + 𝐶𝑅𝐹𝑡−1 ∗ ∆𝐸𝑅𝑡 . This expression is algebraically equivalent to the one we use in our study.
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t
CRD∆ER t
= ∑ CRFi ∗ ∆ER i
(7)
i=0
These two new time series together encompass the stochastic behavior of domestic price of crude oil, i.e. CRD∆CRF + CRDt∆ER = CRDt . t Given the above disaggregation of crude prices in foreign currency, we modify equation (2) as follows: 𝑃𝑡 = 𝛼0 + 𝛼2 𝐶𝑅𝐷𝑡∆𝐶𝑅𝐹 + 𝛼3 𝐶𝑅𝐷𝑡∆𝐸𝑅 + 𝜖𝑡
(8)
α2 and α3 represent cost pass-through from international crude price and exchange rate, respectively. The dynamic formulation of equation (8) can be written in an autoregressive distributed lag (ARDL) model in levels such that: p
q
Pt = β0 + ∑ βi Pt−i + i=1
∑ γi CRD∆CRF t−i i=0
q ∆ER + ∑ 𝜑i CRDt−i +εt
(9)
i=0
In this formulation p and q refer to the number of lags, whereas the regression coefficients βi , γi and 𝜑i measure the short-run impacts of lagged retail price changes, lagged crude oil price changes arising from movements in international crude prices and lagged crude oil price changes arising from movements in exchange rate, respectively. In case a cointegrating relation exists between Pt and 𝐶𝑅𝐷t , the estimation of equation (9) via ordinary least squares would yield superconsistent results. However, the estimates do not have standard asymptotic distribution and may be seriously biased in small samples; therefore, it is customary to use error correction representation for estimation. The error-correction representation of equation (9) is: 𝑝−1 ∆𝐶𝑅𝐹 ∆𝐸𝑅 ∆𝑃𝑡 = 𝜃(𝑃𝑡−1 − 𝛼0 − 𝛼2 𝐶𝑅𝐷𝑡−1 − 𝛼3 𝐶𝑅𝐷𝑡−1 ) + ∑ 𝛽𝑖 ∆𝑃𝑡−𝑖 𝑖=1 𝑞−1
𝑞−1
(10)
∆ER + ∑ 𝛾𝑖 ∆CRD∆CRF t−i + ∑ 𝜑i ∆CRDt−i + 𝜀𝑡 𝑖=0
𝑖=0
∆ indicates the first difference operator and θ represents the long-run equilibrium adjustment q
parameter, where θ = −(1 − ∑i=1 βi ). If retail price is above its equilibrium level, then it should fall back to the long-run equilibrium, whereas if it is below the level forecast by the refined price, then it should rise. The benefit of estimating an error correction representation as opposed to 13
estimating equation (9) is that; if the variables are cointegrated, all terms in equation (10) are stationary and consequently all estimators have standard asymptotic distribution. This enables the estimation of long-run and short-run impacts within a unified framework.
6. Estimation Method In standard cointegration estimation, the relation between the variables is assumed to be symmetric both in the short-run and in the long-run, as it is the case in equations (8) and (10). In our empirical analysis, we employ an asymmetric cointegration approach proposed by Shin et al. (2014), which is based on the decomposition of explanatory variables into partial sums. In this approach, asymmetry is assumed to be present both in the short- and long-run coefficients; therefore, we use a nonlinear auto regressive distributed lag model (NARDL) to model a long-run dynamic relation and an associated short-run error correction relation. We consider two cases; i.e. one threshold and two-threshold cases. In the former case, the explanatory variables are decomposed into two parts around zero where one cumulates positive changes and the other one cumulates negative changes, which can be represented as follows: 𝑋𝑡+ = ∑𝑡𝑖=0 ∆𝑋𝑖+ , where ∆𝑋𝑡+ = ∆𝑋𝑡 ∗ 𝐼∆𝑋𝑡>0
(11)
𝑋𝑡− = ∑𝑡𝑖=0 ∆𝑋𝑖− , where ∆𝑋𝑡− = ∆𝑋𝑡 ∗ 𝐼∆𝑋𝑡≤0
(12)
Here, 𝑋𝑡 is the explanatory variables of the model, i.e. CRDt∆CRF and CRD∆ER . 𝐼{𝑍} denotes an t indicator function which takes the value of 1 if the condition is satisfied and 0 otherwise. Note that ∆𝑋 = ∆𝑋𝑡+ + ∆𝑋𝑡− and 𝑋 = 𝑋𝑡+ + 𝑋𝑡− . The asymmetric cointegrating relation and the associated error correction representation are: 𝑌𝑡 = 𝛼0 + 𝛼1+ 𝑋𝑡+ +𝛼1− 𝑋𝑡+ + 𝜀𝑡 𝑝−1 + − ∆𝑌𝑡 = 𝛽0 + 𝛽1 𝑌𝑡−1 + 𝛽2+ 𝑋𝑡−1 + 𝛽2− 𝑋𝑡−1 + ∑ 𝛾𝑖 ∆𝑌𝑡−𝑖 + 𝑖=1
𝑞−1 + ∑ 𝜑𝑖+ ∆𝑋𝑡−𝑖 𝑖=0
(13) 𝑞−1 − + ∑ 𝜑𝑖− ∆𝑋𝑡−𝑖 + 𝜀𝑡
(14)
𝑖=0
In the two thresholds case, the explanatory variables are decomposed into three parts. 𝑋𝑡+ = ∑𝑡𝑖=0 ∆𝑋𝑖+ , where ∆𝑋𝑡+ = ∆𝑋𝑡 ∗ 𝐼∆𝑋𝑡>∆𝑋
(15)
𝑋𝑡− = ∑𝑡𝑖=0 ∆𝑋𝑖− , where ∆𝑋𝑡− = ∆𝑋𝑡 ∗ 𝐼∆𝑋𝑡≤∆𝑋
(16)
𝑋𝑡∓ = ∑𝑡𝑖=0 ∆𝑋𝑖∓ , where ∆𝑋𝑡− = ∆𝑋𝑡 ∗ 𝐼∆𝑋≤∆𝑋𝑡≤∆𝑋
(17)
The upper and lower thresholds are denoted by ∆𝑋 and ∆𝑋, respectively. The main advantage of two-threshold decomposition is that it enables modeling of not only the asymmetry but also 14
nonlinearity in retail pricing. A nonlinear price adjustment may be driven by a “wait-and-see” strategy of the companies, which neglects minor changes in crude price and/or exchange rate until some pain threshold is passed. Above this threshold the pass through to retail prices would be rapid. The cointegrating relation and its error correction representation in the case of two thresholds are: 𝑌𝑡 = 𝛼0 + 𝛼1+ 𝑋𝑡+ +𝛼1− 𝑋𝑡+ + 𝛼1∓ 𝑋𝑡∓ + 𝜀𝑡
(18) 𝑝−1
∆𝑌𝑡 = 𝛽0 + 𝛽1 𝑌𝑡−1 +
+ 𝛽2+ 𝑋𝑡−1
+
− 𝛽2− 𝑋𝑡−1
+
∓ 𝛽2∓ 𝑋𝑡−1
+ ∑ 𝛾𝑖 ∆𝑌𝑡−𝑖 𝑖=1
𝑞−1
𝑞−1
𝑞−1
(19)
+ ∓ − + ∑ 𝜑𝑖+ ∆𝑋𝑡−𝑖 + ∑ 𝜑𝑖− ∆𝑋𝑡−𝑖 + ∑ 𝜑𝑖∓ ∆𝑋𝑡−𝑖 + 𝜀𝑡 𝑖=0
𝑖=0
𝑖=0
We estimate equation (17) and equation (19) using ordinary least squares method. The existence of cointegrating relation is tested using Pesaran et al. (2001) bounds testing approach. The long run coefficients in equation (16) are estimated using the parameter estimates of equation (17) as 𝛼0 = −𝛽0 /𝛽1 , 𝛼1+ = −𝛽2+ /𝛽1 , 𝛼1− = −𝛽2− /𝛽1 in the case of one threshold. In the case of two thresholds, the long-run coefficients in equation (18) are estimated using the parameter estimates of equation (19) as 𝛼0 = −𝛽0 /𝛽1 , 𝛼1+ = −𝛽2+ /𝛽1, 𝛼1− = −𝛽2− /𝛽1and 𝛼1∓ = −𝛽2∓ /𝛽1 .
7. Empirical Results and Discussion A critical step in this asymmetric cointegration is the determination of threshold(s) that separates partial sum(s). In case of a single threshold, the common approach in the literature is using zero as the threshold value. For the two-threshold case various approaches have been proposed in the literature. Fedoseeva and Werner (2014) propose one standard deviation of ∆𝑋 where Verheyen (2013) suggests 0.30 and 0.70 quantile of ∆𝑋. Bagnai and Ospina (2015) recommend a symmetric quantile band of 𝑥 and 100 − 𝑥, where 𝑥 is determined by data. In this study, following the common practice in the literature, we use zero as the threshold level in the case of one threshold. For the two-threshold case, we rely on data and use a grid search approach to find the threshold levels that minimize the sum of squared residuals. Accordingly, we find lower and upper quantiles as 0.280 and 0.702 (which correspond to -0.80 and 0.74 percent daily change) for CRD∆CRF and 0.204 and 0.732 (which correspond to -0.46 and t 15
0.33 percent daily change) for CRDt∆ER , respectively. The lag lengths for each explanatory variable are chosen by minimizing the Akaike Information Criterion (AIC) subject to the constraint that there is no serial correlation. The estimation results for long-run and short-run coefficients, cointegration tests and diagnostic statistics are presented in Table 3 for all three models. Column 1 represents the base model, the symmetric case. Column 2 shows the one threshold case, where the asymmetry is around zero. Column 3 shows the two-threshold case that captures both the asymmetry and the strategic pricing. Following Pesaran et al. (2001) bounds testing approach, we use two statistics to test for the existence of cointegration: F- and t-statistics. The former corresponds to the joint significance of the coefficients of the long-run relationship, whereas the latter measures the significance of the error correction term. In the case of F-statistic, a value above the upper critical level points to a long-run relationship, one below the lower critical level indicates no cointegration. A value within the bounds makes the test inconclusive. The t-test is recommended by Pesaran et al. (2001) to complement the F-test. A t-statistics above the upper bound provides further confirmation for the existence of a long-run relationship. Both F-statistics and t-statistics are outside the bounds in all three models at 1% significance level. Consequently, the null hypothesis of no cointegration is strongly rejected. We start the discussion of estimation results with the symmetric case (column 1). The long-run coefficients indicate that a 1 TL change in domestic price of crude oil due to movements in international crude oil price leads to a 0.87 TL change in retail price of gasoline in the longrun, whereas a similar change in the domestic price of crude oil due to movements in exchange rate has an impact of 1.13 TL. The difference is significant at 1% indicating that pass-through from exchange rate is higher than pass-through from international crude oil price. We attribute this result to the close link between exchange rate and gross margin of oil companies. Clearly, exchange rate variations do affect the general price level of all products and services, shifting the marginal cost of production. Cost of crude oil is only one (albeit the most important) component of such costs. Hence, the practice of embedding the impact of crude price and exchange rate would yield biased results. In the short-run, the adjustment parameters are close to one and similar in magnitude. This result suggests that the higher impact of exchange rate on retail prices is a structural phenomenon rather than a conjectural one. 16
In column 2, we present the results for the asymmetric case with one threshold, which is associated with two regimes: positive and negative. The long-run coefficients are similar to the symmetric case in magnitude: a 1 TL rise in domestic price of crude oil due to movements in international price leads to a 0.84 TL change in retail price of gasoline in the long-run, whereas a similar decline reduces the retail price by 0.86 TL. In the case of exchange rate, these figures are 1.12 TL and 1.13 TL, respectively. The difference between the coefficients of international price of crude oil and exchange rate is significant at 1% in the case of positive movements and significant at 10% in the case of negative movements. Within each variable, however, the difference between the coefficients of positive and negative components is not significant. In the case of short-run adjustments, 1 TL rise in domestic price of crude oil due to movements in international prices leads to a 0.81 TL change in retail price of gasoline whereas a similar decline reduces retail price by 0.87 TL. For exchange rate, short-run adjustments are 0.98 TL and 0.83 TL, respectively. These figures indicate that currency depreciations increase gasoline prices more than currency appreciations (in absolute values). Impulse response functions presented in Figure 2 also support this finding. A one unit positive shock to exchange rate, i.e. currency depreciation, transforms into retail price hike very rapidly; in fact, it takes only 5 days to observe a one unit increase at the pump. A one unit negative shock, in the meantime, leads only 0.58 unit decline at the pump after 5 days 0.70 unit decline after 10 days. Changes in international price of crude oil, on the other hand, affect retail prices in a symmetric manner, both in terms of magnitude and duration of the adjustment. In column 3, we present the results for the two threshold case. There are three regimes for each variable; one corresponding to highly positive changes, one corresponding to highly negative changes and the last one in between (i.e. moderate changes in either direction). We find no evidence of cointegrating relation when the regressors show moderate change. The long-run coefficients associated with moderate changes (-0.40 for the international price of crude oil and 0.08 for the exchange rate) are insignificant. These results suggest that there is in fact an inactive band in the pricing strategy. Retail price of gasoline is determined by major changes in explanatory variables, minor changes do not shape the long-run behavior, although they may influence the prices in the short-run as demonstrated by impulse response function in Figure 3. The effect of a unit shock in either variable gradually disappears when the magnitude of the shock is moderate, whereas the impact on retail price is permanent if the shock is above the 17
threshold. There is evidence of asymmetry with regards to exchange rate in the long-run, where the coefficients 1.10 in the case of depreciation vs. 1.02 in the case of appreciation; and also in the short with coefficients 1.02 vs 0.72, respectively. Depreciation in TL changes the pump price at a higher and faster way than appreciation.
8. Robustness We check the robustness of our results by adding additional control variables, which are found to be significant in explaining the retail price of gasoline in some studies. These variables are seasonal factors, volatility, and capacity utilization. In the literature the effect of volatility on pricing is ambiguous. On the one hand, it may reduce the probability of collusion in an oligopolistic price setting, thereby reducing price asymmetry. On the other hand, it makes it more difficult for consumers to look for and find the best retail price, which increases the asymmetric pricing (Romano and Scandurra, 2012). We measure volatility in exchange rate and international prices of crude oil separately using a GARCH model that estimates the conditional standard deviation. Then, we include both volatility measures in the model as control variables. However, the coefficients of both variables are insignificant. Following Kaufmann and Laskowski (2005), we examine the role of seasonal idiosyncratic factors by including a set of seasonal dummy variables. These are week-day dummies, day-of-the-month dummies and seasonal dummies. All the seasonal dummies are found to be insignificant. Finally, we use capacity utilization rate of the refineries as a proxy for supply-demand mismatch in the petroleum products sector to investigate whether pricing of companies are influenced by capacity constraints or slacks. We find no evidence that capacity utilization plays any role in pricing decisions.
9. Conclusion The discussion on a potential asymmetry between the prices of crude oil and gasoline has always been an issue for all agents in the economy; however, the discussion intensifies especially in times of high volatility in oil market. Therefore, many studies in the literature empirically test the assertion by Bacon (1991) whether retail prices rise like a rocket, but falls like a feather. The literature, however, does not provide an unequivocal answer. While the first group hardly finds 18
an asymmetric relationship between crude price and retail price of gasoline, the second group confirms the suggestion of Bacon (1991). The third group, in the meantime, conditions this assertion on some other determinants. The literature investigating the reasons behind this heterogeneity among empirical studies identifies a long list of alternative explanations ranging from the estimation method to the time frequency of data. Therefore, it is necessary to clarify the channels through which the crude oil price may affect the retail price and enrich the empirical literature with additional countryspecific analysis. In this study, we provide empirical evidence on the relationship between the cost of crude oil and retail price of gasoline in Turkey. While examining the nature of pricing in gasoline market, we disentangle the crude price in foreign currency and exchange rate since in a small open economy profit margin and other costs are possibly functions of exchange rate. We employ a partial decomposition approach and estimate the determinants of retail prices via a nonlinear autoregressive distributed lag model. This methodology enables us to uncover the relation both in the long-run and in the short-run. Furthermore, we address the presence of strategic pricing where pass-through to retail prices not only depends on the sign, but also the size of shocks. We find that the impacts of crude oil price and exchange rate on the retail price of gasoline differ in a significant way. In particular, the transmission of an exchange rate shock to price of gasoline is more rapid and more significant in magnitude compared to that of a shock to crude price in foreign currency. Therefore, it is highly required to separate the two to obtain unbiased results. Moreover, our results show that the source of asymmetric relationship between the cost of crude oil and retail price of gasoline is exchange rate. In the case of depreciation in domestic currency, the retail price of gasoline responds more rapidly with a larger magnitude compared to that in the case of appreciation. The retail price of gasoline symmetrically responds to the movements in the international price of crude oil. Finally, the estimation results also reveal the existence of an inactive band in pricing strategy, where moderate changes in exchange rate or international price of crude oil are not reflected to the retail prices.
19
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23
Tables and Figures Table 1. Unit Root Tests Test
Level
First Difference -9.43*** -8.58*** -10.92***
Level
First Difference -50.87*** -50.24*** -50.47***
Level
First Difference 0.15 0.29 0.03
Gasoline price C -2.13 -2.12 4.88*** Brent C -1.58 -0.93 0.89*** USD C+t -2.18 -2.02 0.73*** Notes: 1. Null hypothesis is the existence of unit root for ADF and PP, stationarity of the series for KPSS. 2. Modified AIC is used for lag selection. 3. C denotes the test that includes an intercept only. C+T denotes the test that includes an intercept and trend. 4. *** denotes significance at 1%.
Table 2. Unit Root Tests with Structural Breaks Gasoline price Diesel price Brent USD
Test Model C C C T C+T
ZA test -3.60 -3.00 -2.38 -3.40 -3.47
Break 11.26.2010 11.26.2010 5.26.2010 9.13.2007 8.20.2007
P test -4.08 -3.06 -2.36 -3.27 -3.58
Break 11.26.2010 11.26.2010 5.26.2010 9.21.2007 8.17.2007
Notes: 1. ZA denotes Zivot-Andrews unit root. P test denotes Perron unit root test. 2. 𝐻0 for ZA test is unit root versus stationary with structural break. 𝐻0 for P test is unit root with structural break versus stationary with structural break. 3. C denotes the test that includes structural break in the intercept. T stands for the test that includes structural break in the trend. C+T represents the test includes structural break in both the intercept and the trend.
24
Table 3. Regression Results for Gasoline Prices ARDL (4,10,5) Coefficient t-statistics
NARDL-1 (4,7,6,4,5) Coefficient t-statistics
NARDL-2 (4,7,1,6,4,6,5) Coefficient t-statistics
Long-run Coefficients Constant Trend
0.440
13.336***
0.423
5.867***
0.402
7.001***
2.01E-04
0.493
7.22E-04
1.548
0.842
16.266***
0.939
16.744***
-0.400
-0.926
3.14E-05
0.902
𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑪𝑹
0.861
20.171***
𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑬𝑿
1.127
11.316***
𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑪𝑹+ 𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑪𝑹∓ 𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑪𝑹−
0.862
16.481***
0.890
19.057***
𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑬𝑿+
1.117
10.263***
1.102
12.207***
0.081
0.123
𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑬𝑿∓ 𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑬𝑿− Short-run Coefficients 𝑬𝑪𝑻(−𝟏) ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑
𝒑 𝒋=𝟏 𝒒 𝒋=𝟎 𝒒𝟏 𝒋=𝟎 𝒒𝟐 𝒋=𝟎 𝒒𝟑 𝒋=𝟎 𝒓 𝒋=𝟎 𝒓𝟏 𝒋=𝟎 𝒓𝟐 𝒋=𝟎 𝒓𝟑 𝒋=𝟎
1.127
6.414***
1.016
6.950***
-0.037
-5.311***
-0.037
-5.156***
-0.046
-5.965***
∆𝑹𝑬𝑻𝑨𝑰𝑳𝒕−𝒋
-0.249
-4.848***
-0.238
-4.892***
-0.232
-4.836***
∆(𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑪𝑹)𝒕−𝒋
0.977
11.336*** 0.813
7.964***
0.762
7.383***
0.319
1.794*
∆(𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑪𝑹)+ 𝒕−𝒋 ∆(𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑪𝑹)∓ 𝒕−𝒋 ∆(𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑪𝑹)− 𝒕−𝒋 ∆(𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑬𝑹)𝒕−𝒋
0.930
∆(𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑬𝑹)+ 𝒕−𝒋
0.870
9.455***
0.940
9.476***
0.977
6.191***
1.016
6.711***
1.079
1.771*
0.718
3.687***
7.306***
∆(𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑬𝑹)∓ 𝒕−𝒋 ∆(𝑪𝑹𝑼𝑫𝑬(𝑻𝑳)|𝑬𝑹)− 𝒕−𝒋
Cointegration Tests F statistics t statistics Critical values at 1% for F test Critical values at 1% for t test Diagnostics R2 Adjusted R2 AIC 𝛘 𝟐𝐒𝐂 (1) 𝛘 𝟐𝐅𝐅 (1) 𝛘 𝟐𝐍 (2) 𝛘 𝟐𝐇 (1) 𝛘 𝟐𝐀𝐑𝐂𝐇 (1)
0.825
4.160***
7.620*** -5.311*** [4.99, 5.85] [-3.43, -4.10]
5.194*** -5.156*** [3.81, 4.92] [-3.43, -4.60]
4.799*** -5.965*** [3.27, 4.39] [-3.43, -4.99]
0.1758 0.1664 -5.1941 2.78 [0.09] 0.33 [0.56] 96.61 [0.00] 56.42 [0.00] 1.25 [0.26]
0.1813 0.1682 -5.1919 0.37 [0.55] 0.38 [0.54] 106.2 [0.00] 36.92 [0.00] 2.25 [0.17]
0.1913 0.1747 -5.1953 0.07 [0.79] 0.00 [0.96] 100.7 [0.00] 71.53 [0.00] 2.06 [0.15]
25
Notes: 1. OLS results are reported. Standard errors are White-corrected for heteroscedasticity. 2. χ 2SC (1), χ 2FF (1), χ 2N (2), χ 2H (1), and χ 2ARCH (1) denote chi-squared statistics to test for no residual serial correlation (at lag 1), no functional form misspecification, normal errors, homoscedasticity, no ARCH, respectively. 3. p-values are in bracket parentheses. 4. *, **, and *** denote significance at 10%, 5% and 1%, respectively.
Figure 1. Formation of Retail price of Gasoline in Turkey Platt’s Quotation
Crude Oil Price Crude Oil Market
Crude Oil
Customer
+ Retail margin + VAT +
Retail price
+ Transportation and refinery
Retailer
Petroleum Market
+ Distributor margin
Distributor price
+ Refinery margin + EMRA Share + Special Consumption Tax
Distributor
Refinery price
Figure 2. Impulse Response Functions for One Threshold Case – Cumulative (Absolute Value) Change in Retail Prices in Response to a One-Time One Unit Shock to the Exchange Rate and Price of Crude Oil (in US dollar)
Note: y-axis represents the price change and x-axis represents the number of days after the shock
26
Figure 3. Impulse Response Functions for Two-threshold Case – Cumulative (Absolute Value) Change in Retail Prices in Response to a One-Time One Unit Shock to the Exchange Rate and Price of Crude Oil (in US dollar)
Note: y-axis represents the price change and x-axis represents the number of days after the shock
27
