Article history: Received 8 August 2014; last revision 30 September 2014; accepted 6 October 2014

Oil price shocks and the US economy: What makes the latest oil price episode different Maria Dolores Gadea and Ana Gomez-Loscos Abstract: This paper distinguishes different periods in the relationship between oil price shocks, economic growth, and inflation for the US economy. Focusing on the latest period, covering mainly the noughties, a change is found in the exogeneity pattern associated with recent oil price episodes. A significant effect of GDP growth on oil price movements is identified, while there is no evidence of any influence of the latter on either GDP growth or inflation found.

Keywords: oil shocks; inflation; production; structural breaks; exogeneity

JEL Classification: C32, E31, E32, Q43 Introduction The macroeconomic impact of oil price shocks has been widely studied1 and there is a broad consensus among macroeconomists that an increase in oil prices had a significant negative impact on output growth and contributed to high inflation in oil-importing countries during the 1970s, whereas this effect seemed to progressively lessen from the mid-1980s during the tranquil times of the Great Moderation. However, the oil price movements during the noughties have reopened this issue.2 

Maria Dolores Gadea is a Lecturer at the Department of Applied Economics, University of Zaragoza, Spain. E-mail: [email protected].  Ana Gomez-Loscos is a Researcher at the Bank of Spain. E-mail: [email protected]. 1 Two comprehensive surveys of this literature are Hamilton (2008) and Kilian (2008). 2 The past decade has been characterized by a long escalation of oil prices that began in 2002-03 and reached its peak with the spike of 2007-08. Oil prices collapsed at the end of 2008, climbed anew soon afterwards until early 2010 and then began to edge down again. More specifically INTERNATIONAL ECONOMICS LETTERS October 2014 Volume: 3, Issue: 2, pp. 36 - 44 www.ielonline.eu ISSN: 1805-7306

GADEA & GOMEZ-LOSCOS

Economies have faced substantial transformations associated with globalization since the 1970s through which new patterns of production, new technologies, new economic policies and new labour markets have been developed. A changing relationship over time should be expected in the effect of oil price shocks on the economy as a result a decrease of labour market rigidities, changes in monetary policy with explicit commitments regarding inflation, a decline of the oil share in the economy, higher energy efficiency, a reduction in the exchange rate pass-through and the growing demand for crude oil.3 Additionally, some of the existing literature sustains that major oil price movements were caused by physical disruptions of supply brought about by geopolitical events in the Middle East4 and so they are considered exogenous when evaluating the response of macroeconomic variables to oil price changes.5 Nevertheless, it has become widely accepted in recent years that oil price is endogenous with respect to macroeconomic aggregates.6 Bearing this in mind, the contribution of this paper is threefold. For the US economy, we first analyse whether direction of causality between oil prices and the macroeconomy (GDP growth and inflation) evolves over time. Second, we test how this could affect the changing relationship over time both in the response of output growth and inflation to oil price shocks, that is, we look for structural breaks in the relationship between these three variables. We focus particularly on the differences between the decade of the 1970s, when supply disruptions were very common, and the first years of the new millennium, characterized by the growing demand for oil from emerging Asia. As oil prices could be either endogenous or exogeneous with respect to the macroeconomic variables considered, we do not assume either of the two possibilities a priori but we let the data speak to choose the correct specification. Additionally, the wide span used in the paper allows us to cover the most recent oil price rise (2009-10) and the economic developments during a period of particular relevance for recent policy discussions: the GR and the subsequent nascent recovery. focused on the events of 2007-08, Hamilton (2009) explores their causes and consequences, concluding that this episode contributed to the US economic downturn. Clark (2009) also considers these movements as one of the underlying factors that contributed to the Great Recession (GR). 3 The literature on this time-varying relation is extensive (see, Blanchard and Galí (2010) and Baumeister and Peersman (2013), amongst others). For a summary of the possible reasons for the changing relationship, please see Gomez-Loscos et al. (2012). 4 The Yom Kippur War in 1973, the Iranian revolution in 1978, Iraq’s invasion of Iran in 1980 and Iran’s invasion of Kuwait in 1990. 5 This view is held in the extensive oil-related literature developed by Hamilton (see, for example, Hamilton (1983) or Hamilton (2003)). 6 To quote some, Hamilton (2009)] considers that the 2007-08 oil price surge was caused by strong demand colliding with stagnating world oil production. Kilian (2009) considers that the oil price surge that started in 2002, to which the US economic proved resilient, was driven by a series of positive demand shocks associated with shifts in global economic activity, and he argues against the notion that earlier oil price episodes were driven primarily by unexpected supply disruptions.

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OIL PRICE SHOCKS AND THE US ECONOMY

Methodology and results

Exogeneity analysis Taking into consideration the debate about the exogeneity of oil prices, we test the causality of oil prices in relation to output growth and inflation for our whole sample.7 Our US data span is quarterly and runs from 1970.Q1 to 2012.Q4. Oil prices (OILP) quoted in this paper refer to the Producer Price Index for crude petroleum from the US Bureau of Labor Statistics. GDP and consumer price index (CPI) are from OECD’s Main Economic Indicators.8 Data are displayed in Figure 1. Figure 1: Year-on-year growth rates

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Source: Own results To analyze causality, we perform a rolling exercise. The following stationary VAR system is defined: 𝑝

𝑦𝑡 = 𝜐 + ∑𝑖=1 𝐴𝑖 𝑦𝑡−𝑖 + 𝑢𝑡

(1)

Although the difference between causality and exogeneity is really subtle, many papers, including Sims (1975) refer to causality tests as exogeneity tests. See Wu (1983) for a survey. 8 Unit root tests do not reject that the growth rates of GDP, consumer price index and oil price are stationary. Using different oil price series, such as the West Texas Intermediate and the Brent price does not significantly alter the results. Until mid-1980s, the link between oil prices and economic environment was studied using linear models, but as the relationship began to wane, some authors introduced non-linear transformations of oil prices. These statistical transformations could help to extract the exogenous component of oil prices (see Kilian (2008)). We work with the original series in order that the results are not influenced by these statistical devices (for a review, see Jiménez-Rodriguez and Sánchez (2005)). 7

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where 𝑦𝑡 = (𝑦1𝑡 , . . . , 𝑦𝑛𝑡 )′ is a 𝑛𝑥1 random vector, 𝐴𝑖 are fixed 𝑛𝑥𝑛 coefficient matrices, 𝜐 = (𝜐1 , . . . , 𝜐𝑛 )′ is a fixed 𝑛𝑥1 vector of intercept terms, 𝑢𝑡 = (𝑢1𝑡 , . . . , 𝑢𝑛𝑡 )′ is a 𝑛 −dimensional innovation process with 𝐸(𝑢𝑡 ) = 0, 𝐸(𝑢𝑡 𝑢𝑡 ′) = ∑𝑢 , 𝐸(𝑢𝑡 𝑢𝑠′ ) = 0 for 𝑠 ≠ 𝑡 and, finally, 𝑝 is the order of the VAR.9 In our case, 𝑦𝑡 = (Δ𝐺𝐷𝑃, Δ𝐶𝑃𝐼, Δ𝑂𝐼𝐿𝑃)′ . In this context, a Wald test for Granger causality is proposed: 𝐻1 : 𝛼31,1 = 𝛼31,2 = 0 for causality from Δ𝐺𝐷𝑃 to Δ𝑂𝐼𝐿𝑃 𝐻2 : 𝛼32,1 = 𝛼32,2 = 0 for causality from Δ𝐶𝑃𝐼 to Δ𝑂𝐼𝐿𝑃 (2) 𝐻3 : 𝛼31,1 = 𝛼31,2 = 𝛼32,1 = 𝛼32,2 = 0 for causality from Δ𝐺𝐷𝑃 and Δ𝐶𝑃𝐼 to Δ𝑂𝐼𝐿𝑃 where 𝛼𝑖𝑗,𝑙 with i,j=1,...,n and l=1,...,p are the coefficients of matrix 𝐴𝑙 . This test is applied across the entire sample using a rolling method with a window size of 40 quarters. Results of p-values are shown in Figure 2. Figure 2: Rolling causality test

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Source: Own results The results show that oil price exogeneity does not remain stable across the sample. Endogeneity appears in two different periods, one at the beginning of the sample, due to the influence of inflation reversals in the 1970s, and the other at the end of the sample, as a result of the effect exerted on the oil price by the output growth. Notice that this latter effect occurs when the rolling window moves across the period 1998.4-2008.3 to 2000.32010.1, that is, for about ten years the oil price is endogenous. However, during the last part of the sample, as we move into the period following the GR, the identified causality of GDP growth on oil prices vanishes. According to the information criteria and the diagnosis of the residuals we have selected p=2. However, the results are also robust to imposing higher orders. 9

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OIL PRICE SHOCKS AND THE US ECONOMY

Identification of structural breaks From the findings of the previous section, it is immediate to question whether the evolution and causal links among the variables could help us to identify different periods in the relationship between oil prices and macroeconomic variables. To address this question, we consider the methodology developed by Qu and Perron (2007) (QP), which allows us to estimate and test for multiple structural changes that occur at unknown dates in a system of equations. 10The method of estimation we use is quasimaximum likelihood based on Normal errors. The model considered is as follows: 𝑦𝑡 = (𝐼 ⊗ 𝑧𝑡′ )𝑆𝛽𝑗 + 𝑢𝑡

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(3)

There are 𝑛 equations and 𝑇 observations, excluding the initial conditions if lagged dependent variables are used as regressors. The total number of structural changes in the system is 𝑚 and the break dates are denoted by the 𝑚 vector, 𝑇 = (𝑇1 , . . . , 𝑇𝑚 ) taking into account that 𝑇0 = 1 and 𝑇𝑚+1 = 𝑇. A subscript 𝑗 indexes a regime (𝑗 = 1, . . . 𝑚 + 1), a subscript 𝑡 indexes the temporal observation (𝑡 = 1, . . . , 𝑇), and a subscript 𝑖 indexes the equation ( 𝑖 = 1, . . . , 𝑛) to which a scalar dependent variable 𝑦𝑖𝑡 is associated. The parameter 𝑞 is the number of regressors and 𝑧𝑡 is the set which includes the regressors from all equations 𝑧𝑡 = (𝑧1𝑡 , . . . , 𝑧𝑞𝑡 ). Furthermore, 𝑢𝑡 has mean 0 and covariance matrix ∑𝑗 for 𝑇𝑗+1 + 1 ≤ 𝑡 ≤ 𝑇𝑗 . The matrix 𝑆 is of dimension 𝑛𝑥𝑞 with full column rank. We use a selection matrix that involves elements that are 0 and 1 and, thus, indicates which regressors appear in each equation. For our VAR model, we further have 𝑧𝑡 = (𝑦𝑡−1 , . . . , 𝑦𝑡−𝑞 ), which contains simply the lagged dependent variables and the deterministic terms and 𝑆, which is an identity matrix. We consider two specifications. Firstly, for a full VAR system where all the variables are endogenous, 𝑧𝑡 = (1, Δ𝐺𝐷𝑃𝑡−1 , Δ𝐺𝐷𝑃𝑡−2 , Δ𝐶𝑃𝐼𝑡−1 , Δ𝐶𝑃𝐼𝑡−2 , Δ𝑂𝐼𝐿𝑃𝑡−1 , Δ𝑂𝐼𝐿𝑃𝑡−2 ) and 𝑆 = 𝐼21. Secondly, we apply the QP method to a bivariate VAR with two endogenous variables (Δ𝐺𝐷𝑃 and Δ𝐶𝑃𝐼 inflation) and an exogenous variable (Δ𝑂𝐼𝐿𝑃). Hence, our model is: 𝑝

𝑝

𝑦𝑡 = 𝜐 + ∑𝑖=1 𝐴𝑖 𝑦𝑡−𝑖 + ∑𝑖=0 𝐵𝑖 𝑥𝑡−𝑖 + 𝑢𝑡 (4)

Archanskaïa et al. (2012) use this methodology to test for the presence of structural breaks in the relationship between oil prices and an index of global economic activity, while GómezLoscos et al. (2011) test oil price and macroeconomics relation for the Spanish economy. 10

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where 𝑦𝑡 = (Δ𝐺𝐷𝑃, Δ𝐶𝑃𝐼)′ , 𝑥𝑡 = (Δ𝑂𝐼𝐿𝑃) and 𝑆 = 𝐼16.11 The number of breaks has been selected following approximated critical values derived from response surface regressions.12 The results are quite robust with either of the two specifications: 3 breaks are found and their location from the global optimization is in 1979.4, 1991.2 and 2000.4 when the oil price variable is exogenous and in 1982.3, 1991.1 and 1999.3 when it is considered endogenous. Then, we test the causality in the different periods defined by the breaks. Results in Table 1 confirm the findings of the rolling procedure; there is endogeneity at 10% at the beginning of the sample due to the link between inflation and oil prices and a strong causality from economic growth to oil price in the last period. Table 1: Granger causality test by periods Exogenous VAR Endogenous VAR Wald Wald Period Period test test 1970.15.41 1970.19.37 1979.4 (0.2474) 1982.3 (0.0542) 1980.17.69 1982.41.29 1991.2 (0.1034) 1991.1 (0.8623) 1991.37.60 1991.22.27 2000.4 (0.1072) 1999.3 (0.6855) 2000.18.81 1999.413.16 2012.4 (0.0659) 2012.4 (0.0105) Note: p-values are in brackets. Source: Own results

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Focusing on recent oil price episode Bearing in mind the discussion in the literature about recent oil price shocks, we present more detailed figures for the last identified period that mainly covers the noughties and pinpoints a different pattern for these events. Results in Table 2, considering the causality effect of each variable separately, support the previous finding of the effect of GDP growth on oil prices and provide a basis for ordering the variables properly, carrying out the Cholesky decomposition and calculating impulse response functions (Figure 3).13 We observe a strong positive and significant effect of GDP growth on oil prices in a period in which there is a high demand from emerging countries at a time when the growing fragmentation of international production makes that the links between economies increase. However, there is no significant We have chosen to impose 2 lags in accordance with several information criteria as in the full VAR system. 12 The details of the results, which are not presented to save space, are available from the authors upon request. 13 This order in the VAR system is (GDP, CPI, OILP). Alternative identification schemes lead to similar findings. Confidence intervals have been estimated by bootstrapping. 11

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influence of oil prices on the macroeconomic variables,14 which, as mentioned in the introduction, could be due to the declining share of oil, the origin of the shocks or the commitment of central banks to stabilizing inflation, amongst others. When comparing this period with the 1970s -when rising energy prices were associated with supply disruptions in the Middle East-, the effect of GDP on the oil price is not significant, whereas an effect of inflation on the oil price is identified. This could be related to the inflationary pressures that began at the end of the 1960s associated with the increase in commodity prices and the collapse of Bretton Woods.15 Table 2: Granger causality test 1999.4-2012.4 GDP CPI OILP ALL 6.54 1.12 9.48 GDP (0.0380) (0.5762) (0.0502) 9.48 1.19 11.72 CPI (0.0087) (0.5503) (0.0195) 7.25 3.41 13.16 OILP (0.0267) (0.1817) (0.0105) Note: Causality effects of the three variables separately and the global effect (columns) on each of the variables (rows); p-values are in brackets. Source: Own results 42

Figure 3: Impulse-response functions

14 15

We find bidirectional causality between the two macro variables of the system. See Barsky and Kilian (2002) for more details.

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Source: Own results Concluding remarks This paper shows that recent episodes of oil price movements that occurred during the noughties have a different nature to previous shocks. During this period, economic activity exerts some influence on oil prices (and also on inflation), which is not identified in previous periods, while there is no evidence of any effect of the oil prices on either output growth or inflation. New causal relationships between macro-aggregates and oil prices call for a reexamination of the role of oil price shocks on macroeconomic outcomes. The large shocks of oil prices do not seem to be a potential cause of the recent rise in the volatility of the US economy, associated to the GR, in the light of these results.

Acknowledgements Financial support from Ministerio de Ciencia e Innovación under grant ECO2011-30260-C03-02 is gratefully acknowledged. The views expressed in this paper are the responsibility of the authors and do not necessarily represent those of the Banco de España or the Eurosystem. 43 References Archanskaïa, E., Creel, J., and Hubert, P. (2012). The nature of oil shocks and the global economy. Energy Policy 42(C), 509–520. Barsky, R. B., and Kilian, L. (2002). Do we really know that oil caused the great stagflation? a monetary alternative.” In B. S. Bernanke, and K. Rogoff (Eds.), NBER Macroeconomics Annual 2001, 16, 137–83, Cambridge, MA: MIT Press. Baumeister, C., and Peersman, G. (2013). Time-Varying Effects of Oil Supply Shocks on the US Economy. American Economic Journal: Macroeconomics 5(4), 1–28. Blanchard, O., and Galí, J. (2010). Labor Markets and Monetary Policy: A New Keynesian Model with Unemployment. American Economic Journal: Macroeconomics 2(2), 1–30. Clark, T. E. (2009). Is the great moderation over? an empirical analysis. Economic Review Federal Reserve Bank of Kansas City, Q IV, 5–42. Gomez-Loscos, A., Gadea, M. D., and Montañes, A. (2012). Economic growth, inflation and oil shocks: are the 1970s coming back? Applied Economics 44(35), 4575–4589. Gómez-Loscos, A., Montañés, A., and Gadea, M. D. (2011). The impact of oil shocks on the Spanish economy. Energy Economics 33(6), 1070–1081. Hamilton, J. D. (1983). Oil and the macroeconomy since World War II. Journal of Political Economy 91(2), 228–48. www.ielonline.eu

OIL PRICE SHOCKS AND THE US ECONOMY

Hamilton, J. D. (2003). What is an oil shock? Journal of Econometrics 113(2), 363–398. Hamilton, J. D. (2008). Oil and the macroeconomy. In S. N. Durlauf, and L. E. Blume (Eds.), The New Palgrave Dictionary of Economics, Palgrave Macmillan. Hamilton, J. D. (2009). Causes and consequences of the oil shock of 2007-08. Brookings Papers on Economic Activity 40(1), 215–283. Jiménez-Rodriguez, R., and Sánchez, M. (2005). Oil price shocks and real GDP growth: empirical evidence for some oecd countries. Applied Economics 37(2), 201–228. Kilian, L. (2008). The economic effects of energy price shocks. Journal of Economic Literature 46(4), 871–909. Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. American Economic Review 99(3), 1053–69. Qu, Z., and Perron, P. (2007). Estimating and testing structural changes in multivariate regressions. Econometrica 75(2), 459–502. Sims, C. A. (1975). Exogeneity tests and multivariate time series: Part 1. Discussion Paper Center for Economic Research, University of Minnesota, 75-54. Wu, D.-M. (1983). Tests of causality, predeterminedness and exogeneity. International Economic Review 24(3), 547–58. 44

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