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O R I G I N A L P A P E R
Determining the asymmetric effects of oil price changes
on macroeconomic variables: a case study of Turkey
Yeliz Yalcin • Cengiz Arikan • Furkan Emirmahmutoglu
Published online: 18 October 2014 Springer Science+Business Media New York 2014
Abstract This paper aims to investigate the effects of unanticipated oil price
changes on the Turkish economy using quarterly gross domestic product (GDP) and
monthly consumer price index (CPI) and real exchange rate (RER) for the period
2002–2013. While the bulk of previous studies have employed the standard meth-
odology without true data generating process knowledge, in this study asymmetric
Vector Autoregressive methodology proposed by Kilian and Vigfusson (Quant Econ
2(3): 419–453, 2011) is used to analyze the asymmetric impact of oil prices onmacroeconomic aggregates. This method allows the researcher to investigate the
asymmetric effects of innovations in oil prices on variables without knowing data
generating process is linear or not. Empirical findings that, the oil prices changes
have asymmetric effects on CPI and RER at one standard deviation shocks in
different periods unlike GDP. These asymmetric effects are also statistically sig-
nificant at 10 % significance level. Specifically, when oil price increases, CPI and
RER increases but GDP decreases in the long term.
Keywords Oil price Turkish economy Asymmetric effect VAR
Y. Yalcin C. Arikan F. Emirmahmutoglu (&)Department of Econometrics, Gazi University, 06500 Ankara, Turkey
e-mail: [email protected]
URL: http://websitem.gazi.edu.tr/furkan
Y. Yalcin
e-mail: [email protected]
URL: http://websitem.gazi.edu.tr/yyeliz
C. Arikan
e-mail: [email protected]
C. Arikan
Turkish Ministry of Customs and Trade, Ankara, Turkey
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DOI 10.1007/s10663-014-9274-y
http://crossmark.crossref.org/dialog/?doi=10.1007/s10663-014-9274-y&domain=pdfhttp://crossmark.crossref.org/dialog/?doi=10.1007/s10663-014-9274-y&domain=pdf
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JEL Classification C01 C32 C34 C82
1 Introduction
The effects of changes in oil prices on economic activity receive a considerable
amount of attention by economists and policymakers. Most of the studies assume
that the relationship between oil prices and macroeconomic aggregates is linear and
therefore, estimate it by using standard linear Vector Autoregressive (VAR) model
and cointegration framework (for example Hamilton 1983; Gisser and Goodwin
1986; Bohi 1991 etc.). However, it has been found that the decreases in the oil
prices that take place after the second half of 1980s have smaller positive effects on
economic activity than predicted by usual linear models (Lardic and Mignon 2008).Mork (1989) is the first study to provide the empirical evidence on asymmetric
effects1 of oil price shocks on output. After this influential work, the asymmetric
relationship between oil prices and economic aggregates has begun to gain
importance (for example Mork 1989, 1994; Mory 1993; Hamilton 1996, 2003;
Brown and Yücel 2002; Mehrara 2008; Kilian and Vigfusson 2011).
There is a vast literature to investigate different channels of the asymmetric effect
of oil prices on the macroeconomics variables. First, adjustment costs suggested by
Hamilton (1988) could lead to an asymmetric response to changing oil prices.
Rising (falling) oil prices hinder (stimulate) the economic activities directly.
However, the costs of adjusting to changing oil prices also slow down the economic
activities. Rising oil prices present two negative effects for the economic activities.
Falling oil prices present both negative and positive effects, which would tend to be
offsetting. (Brown and Yücel 2002). Second, the study by Ferderer (1996)
emphasizes that if oil price volatility has an adverse impact on the economic activity
and volatility also rises due to increases and decreases in oil price shock, then
uncertainty has the potential to explain asymmetry. Third, asymmetry may stems
from a monetary policy. The study by Bernanke et al. (1997) indicates that the
Federal Reserve Bank responds more aggressively to the rises in crude oil prices
than it does to falls in the U.S. economy (Herrera et al. 2014).
If the true relation is linear and one mistakenly estimates a nonlinear
specification, the resulting estimates are asymptotically biased (Kilian and
Vigfusson 2011). Moreover, if the true relation is nonlinear and one mistakenly
estimates a linear specification, the resulting estimates are asymptotically biased
(Hamilton 2003). In order to avoid either problem, Kilian and Vigfusson (2011)
suggest a new approach, which consists of including both linear and nonlinear
terms. The biggest advantage of their method is that the impulse responses are
consistent regardless of whether the data generating process is symmetric or
asymmetric. In other words, this method can be used without knowing the nature of the true data generating process (DGP).
1 The asymmetric effect can be defined as increases and decreases in any variable do not have same
effect on any variable or economy.
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In this study in order to examine the asymmetric impacts of oil price fluctuations
on economic activity in Turkey, Kilian and Vigfusson’s (2011) asymmetric VAR
methodology is used. There are several underlying reasons why the Turkish
economy might be an important case study to examine the relationship between oil
prices and macroeconomic aggregates. First, since Turkey can only produce 10 %of its oil demand; it is an important oil importing country when compared to other
OECD countries. Second, oil expenditure has a big part in GDP, as well as the
balance of international payment. Therefore, the changes in oil prices affect the
Turkish economy drastically. Third, in Turkey the biggest portion of special
consumption tax revenues comes from oil and natural gas so the changes in oil price
and exchange rates are crucial for policy makers (Alper and Torul 2010; Berument
et al. 2010).
Empirical evidence regarding the relationship between oil price and macroeco-
nomic aggregates for Turkey is provided by a number of studies that employalternative estimation methods, i.e. different macroeconomic variables and for
different time periods. Even though most of these studies focus on the symmetric
relationship between oil prices and macroeconomic variables,2 the studies on the
asymmetric effects of oil price fluctuations on the Turkish economy are rather
limited. Alper and Torul (2010) examine the asymmetric effects of oil prices on the
manufacturing sector for the period 1990–2007, using VAR models. Their empirical
findings suggest that oil price increases affect several manufacturing sectors
asymmetrically, i.e. wood, furniture, chemical. Catik and Onder (2013)analyze the
asymmetric effects of oil prices on the economic activity for Turkey by using amultivariate two-regime Threshold VAR model. Their results suggest that the
relationship between oil price changes and economic activity is nonlinear and shows
an asymmetric pattern.
The purpose of this study to investigate the asymmetric effects of oil price
changes on the macroeconomic aggregates including real gross domestic products,
consumer price index and exchange rate; by employing Kilian and Vigfusson’s
(2011) model. To this end, monthly and quarterly data are utilized for the Turkish
economy covering the period 2002–2013. This paper proceeds as follows.
Sections 2 and 3 presents methodology, and the data set and estimation results,
respectively. Conclusions are given in Sect. 4.
2 Methodology
In this study, Kilian and Vigfusson’s (2011) methodology is employed to examine
the relationship between oil price volatility and macroeconomic aggregates for
Turkey, since their model can be applied without knowing the nature of the DGP.
The VAR ( p) model in Kilian and Vigfusson (2011) is:
2 There is a literature on the symmetric impacts of oil prices on macroeconomic aggregates for Turkey.
See, for example, Alper and Torul 2008; Berument and Taşçı 2002; Diboglu and Kibritcioglu 2003;
Kibritcioglu 2003; Özlale and Pekkurnaz 2010; Yaylali and Lebe 2012, Gökçe 2013).
Empirica (2015) 42:737–746 739
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X t ¼ a10 þX pi¼1
a1iY t i þX pi¼1
a2i X t i þ e1t
Y t ¼ b
10þX p
i¼1
b1iY t i
þX pi¼1
b2i X
t iþX p
i¼0
g2i X þ
t iþ e
2t
ð1Þ
where the X t and Y t is the percentage in oil price and the macroeconomic variable,
respectively, and et WN 0;Rð Þ. While the first equation is standard linear VAR, thesecond equation includes both oil price increase and decrease effects by X t and X
þt .
Here X þt is a censored variable and can be defined as fallows
X þt ¼ X t ; X t [ threshold value
0; X t threshold value
( ð2Þ
where threshold value can be estimated by using Chan (1993) or can be taken as
zero. Equation (1) can be estimated by standard regression since the OLS residuals
of model (1) are uncorrelated (Kilian and Vigfusson 2011).
In order to test whether there is an asymmetric effect, Kilian and Vigfusson
(2011) has suggested two different methods:
2.1 Slope based test
The slope based test does not require the calculation of an impulse response and any
other properties. This test is based on statistical testing the censored variables in
model (1). The null hypothesis for the test:
H 0 : g21;0 ¼ ¼ g21; p ¼ 0
This test has an asymptotic v2 pþ1 distribution. Although slope based tests are
useful to determine the asymmetry, they do not give any idea about the direction and
level of the asymmetry.
2.2 Impulse response based tests
Generalized impulse response functions to both positive and negative oil price
shocks by using model (1) are calculated. The null hypothesis of test:
H 0 : I yðh; dÞ ¼ I yðh; dÞ
where I y h; dð Þ and I y h; - dð Þ are responses of Y t at horizon h ¼ 1; 2; . . .; H to a
shock of d. This test has an asymptotic v2 H þ1 distribution. Against the slope based
test, this test depends on the magnitude of shock. Therefore the impulse response
based test more relative and powerful than the slope based test (Kilian and Vig-fusson 2009).
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3 Data and empirical results
In this study, quarterly data on the real gross domestic product (GDP), monthly data
for the consumer price index (CPI), the real effective exchange rate (RER) and the
brent oil price are used. The data cover the period 2002:01-2013:12.3
Since theseries have seasonal pattern, they are adjusted using the Tramo/Seats method. All
variables are used in logarithm form. The time series properties of all the variables
included in this study are examined using Augmented Dickey-Fuller (ADF, 1981)
and Phillips Perron (PP, 1988) unit root tests. The null hypothesis of the ADF and
PP tests for both the model with constant term and the one with a constant and trend
term is that the time series contains a unit root. The results of these tests are given in
Table 1.
According to unit root tests results, unit root null cannot be rejected at
conventional significance levels, at the 1 % significance level, for all variables.Therefore, the first differences of all the variables are used when constructing the
VAR. After the censored variable is defined by replacing negative values of changes
in oil prices with zero, model (1) has been built-up for each macro-economic
variable and oil price in first differences, separately.4 Since Turkey has no impact on
oil price determination, lags of Y t are excluded from the first equation in model (1).
So the model becomes:
X t ¼ a10 þX p
i¼1
a2i X t i þ e1t
Y t ¼ b10 þX pi¼1
b1iY t i þX pi¼1
b2i X t i þX pi¼0
g2i X þt i þ e2t
ð3Þ
In order to explore the effect of the positive oil price shocks on each macro-
economic aggregate, we calculated the generalized impulse responses. As discussed
in Koop et al. (1996) and Potter (2000), multivariate nonlinear models produce
impulse responses, which are history and shock dependent. Since the model (1) is
nonlinear, the generalized impulse responses are calculated using the procedure
given in Kilian and Vigfusson (2011). The procedure can be outlines as follows:
1. Xi information set, include all lagged values of X t and Y t , is defined.
2. Given Xi two time path X t þh and Y t þh are generated. When generating the first
time path, e1t value is set equal to a predetermined value d. The realizations of
e1;t þhðh ¼ 1; 2; . . .; H Þ are drawn from the marginal empirical distribution of e1t .The realizations of e2;t þhðh ¼ 0; 1; . . .; H Þ are drawn independently from themarginal distribution of e2t . When generating the second time path, all e1;t þh and
e2;t þhðh ¼ 0; 1; . . .; H Þ are drawn from their respective marginal distributions.
3 The data of brent oil price, gross domestic product and consumer price, real effective exchange rate are
obtained from International Energy Agency: http://www.iea.org., The Central Bank of the Republic of
Turkey: http://evds.tcmb.gov.tr/ , respectively.4 Akaike Information Criteria (AIC) is used to determine the optimal lag lengths of the model; the lag
order was 6 for GDP, 2 for CPI and 6 for RER.
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3. Calculate the difference between the time paths for Y t þhðh ¼ 0; 1; . . .; H Þ4. Average this difference across m = 500 repetitions of Steps 2 and 3 (Kilian and
Vigfusson 2011)
The impulse responses of each macro-economic aggregate when one standard
deviation positive shock is given to oil price are plotted out in the left panel of
Fig. 1. The dotted lines show the 95 % confidence intervals5
based on the
bootstrap simulation with 500 trials are calculated for responses to a positive
shock. The history dependent impulse responses are reported for 6 periods6 for
GDP and 12 periods for CPI and RER. In linear models, the impulse response to a
positive shock is by construction the mirror image of the response to a negative
shock of the same type and size (Hove 2012). Therefore, if there is an asymmetry,
positive and negative shocks are not mirror images of one another (Balke et al.
2002). To compare the positive shock and negative shock impulse responses, boththe responses to positive and the responses to negative shocks are given together
in the right panel of Fig. 1. The dotted line shows responses to a negative shock
and the straight line shows the responses to positive shock from the asymmetric
model.
The right panel of Fig. 1 reports the responses to a positive shock, I y h; dð Þ,and theresponses to a negative shock, I y h; dð Þ for GDP, CPI and RER. Although theresponses of GDP to a negative shock are nearly invisible, there are the small
absolute distance between the responses to a positive and a negative shock of the
same magnitude for CPI and RER. These results may provide an evidence for the
Table 1 The ADF and PP unit root test results
GDP CPI RER Oil price
ADF
Level
Constant -0.799 0.090 -2.218 -2.078
Constant and trend -1.860 -1.943 -2.119 -2.339
First difference
Constant -5.158*** -7.967*** -8.285*** -6.435***
PP
Level
Constant -0.832 0.106 -2.343 -1.887
Constant and trend -2.190 -1.780 -2.351 -3.118
First difference
Constant -5.158*** -7.959*** -7.806*** -8.039***
*,**,*** Statistically significant at the 10, 5, 1 % level, respectively
5 We also consider at 90 % confidence levels. However, our results are almost same at both confidence
levels.6 Since the data of GDP is quarterly, the periods of impulse responses are taken for 1.5 year.
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existence of asymmetry for inflation and exchange rate. In order to test whether this
distance is statistically significant, in other words, whether there is an asymmetric
effect of oil price changes on GDP, CPI and RER, impulse response based tests are
used. Since the impulse response based test is more powerful, it is preferred in this
study. The p-values of the test H 0 : I y h; dð Þ ¼ I y h; dð Þ for 8 periods are given inTable 2.
When a one standard deviation shock is considered, the symmetry null
hypothesis cannot be rejected at 5 % significance level for all periods for GDP.
That is, there is no evidence against the symmetry null hypothesis for GDP inresponse to a 1 standard deviation shock except the first period at 10 % significance
level. However, at all periods the null hypothesis can be rejected at 5 % significance
level for CPI and exchange rate. In other words, the impacts of oil price changes on
CPI and RER are asymmetric.
Fig. 1 Effects of oil price shock, asymmetric model
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According to the left panel of Fig. 1, although the changes in oil prices increase the
GDP growth, the cumulative effects of GDP decrease with the total effect of -0.3419
for 12 periods. This result is similar to the findings of Jones et al. (2004) and Lardic and
Mignon (2008). The impacts of oil price shock on CPI and RER are positive, which is
parallel to the literature (see Leblanc and Chinn 2004). According to the right panel of
Fig. 1, the oil price increases have larger impact on both GDP growth and inflation than
the oil price declines. However, the negative oil price shocks have larger effect on RER
than the positive oil price shocks. There are many reasons supporting these results for
Turkey as an oil importer country. Since oil is a necessary good for Turkey and the share
of oil in the budget is large, the negative oil price shocks have bigger impact on the
exchange rate. Also, the taxes imposed by the government on oil prices increased the
pump prices. This increase has further increased the exchange rate.
4 Conclusions
The aim of this paper is to estimate the asymmetric effects of oil price changes on
the macroeconomic variables for a small open economy like Turkey. In order to
investigate the asymmetric impact of oil prices, this study uses an asymmetric VAR
model that is proposed by Kilian and Vigfusson (2011) employing quarterly GDP,
and monthly CPI and RER data for the period 2002–2013. There is a wide
consensus that the effect of oil prices on macroeconomic aggregates is asymmetric.
Unlike the existing literature where the standard methodology is used without
knowing the true DGP, in this study the asymmetric impact of oil prices is
investigated utilizing an asymmetric VAR model. Empirical evidence from the
impulse responses suggests that although the impact of oil price changes on GDP is
symmetric after the first quarter, CPI and RER are affected asymmetrically for allperiods. The positive oil price shock has a smaller positive effect on CPI but larger
positive effect on RER. The GDP is also at first positively affected from a positive
oil price shock, but the overall cumulative effect is negative for 12 periods used in
this study.
Table 2 The impulse response based tests results
Period GDP CPI RER
1 0.0903* 0.0000*** 0.0007***
2 0.2303 0.0000*** 0.0030***
3 0.2382 0.0000*** 0.0024***
4 0.2833 0.0001*** 0.0059***
5 0.2906 0.0002*** 0.0119**
6 0.4014 0.0004*** 0.0230**
7 0.4813 0.0002*** 0.0401**
8 0.5784 0.0005*** 0.0635*
p Values are based on the v2 H þ1 distribution
*, **, *** Statistically significant at the 10, 5, 1 % level, respectively
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