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Does State Dependence Matter in Relation to Oil Price Shocks on Global Economic Conditions?

  • Gilles Dufrénot , William Ginn EMAIL logo , Marc Pourroy and Adam Sullivan
Published/Copyright: April 10, 2024
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Studies in Nonlinear Dynamics & Econometrics
From the journal Studies in Nonlinear Dynamics & Econometrics Volume 29 Issue 2

Abstract

A common thread in the literature shows that an oil price shock can have a major impact on global economic conditions. We examine the global dimensions of changes to the global oil price and world economic uncertainty using three model types: ordinary least square (OLS); general additive model (GAM); and non-linear vector autoregression (VAR) model with local projections (LP). Our study highlights a positive and statistically significant effect of oil prices on economic uncertainty during non-expansionary periods, yet the impact is negative on economic uncertainty during periods of economic growth. Using a VAR-LP we analyze the global dimensions of a world oil price shock on global economic conditions and investigate whether there is consistency in how an oil price shock influences economic growth, consumer prices and economic uncertainty based on the state of economic conditions. The empirical evidence shows that during an expansionary (a non-expansionary) period, the impact of an oil price shock lowers (elevates) economic uncertainty. The empirical evidence from the three model types taken together indicate a presence of state dependence on the influence of an oil price shock.

Keywords: oil prices; business cycles; world economic uncertainty
JEL Classification: E31; E32; Q43
Corresponding author: William Ginn, Artificial Intelligence, Labcorp, Sr. Economist/Data Scientist, 10 Moore Dr., Durham, NC, USA; and Coburg University of Applied Sciences, Coburg, Germany, E-mail:

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Conflict of interest: The authors state no conflict of interest.

  3. Research funding: None declared.

Appendix A

A.1 Regression Results: Oil Inflation and Changes in Crude Oil Production

Table 5 below estimates the impact of global output growth (i.e. β y ) on oil inflation (π O ) and changes to crude oil supply (Δ ln S O ) based on stationary data.

Table 5:

Empirical results (OLS regression).

Dependent variable
π O Δ ln S O
β y 4.597c 0.281c
(0.445) (0.068)
α −5.412b 0.305
(2.374) (0.268)
Adjusted R2 0.337 0.171
F statistic 62.991c 25.526c

  1. a, b, cDenote significance at 10 %, 5 % and 1 %, respectively. OLS robust standard errors are presented in parenthesis.

A.2 Alternative Non-linear Models

Figure 5: Oil price shock based on state-dependence (alternative non-linear model I – partial sample). IRFs depict nonlinear responses from an oil price shock for periods of economic expansion (left panel) and periods of economic slack (right panel).
Figure 5:

Oil price shock based on state-dependence (alternative non-linear model I – partial sample). IRFs depict nonlinear responses from an oil price shock for periods of economic expansion (left panel) and periods of economic slack (right panel).

Figure 6: Oil price shock based on state-dependence (alternative non-linear model II – full sample). IRFs depict nonlinear responses from an oil price shock for periods of economic expansion (left panel) and periods of economic slack (right panel).
Figure 6:

Oil price shock based on state-dependence (alternative non-linear model II – full sample). IRFs depict nonlinear responses from an oil price shock for periods of economic expansion (left panel) and periods of economic slack (right panel).

Figure 7: Transition function based on state-dependence (alternative model II – full sample). The figures shows the weighted regime (i.e. F(z)). Shaded areas indicate OECD recession dates.
Figure 7:

Transition function based on state-dependence (alternative model II – full sample). The figures shows the weighted regime (i.e. F(z)). Shaded areas indicate OECD recession dates.

Figure 8: Oil price shock based on state-dependence (alternative non-linear model III – partial sample). IRFs depict nonlinear responses from an oil price shock for periods of economic expansion (left panel) and periods of economic slack (right panel).
Figure 8:

Oil price shock based on state-dependence (alternative non-linear model III – partial sample). IRFs depict nonlinear responses from an oil price shock for periods of economic expansion (left panel) and periods of economic slack (right panel).

Figure 9: Posterior means of coefficients (alternative model VI). Posterior means of coefficients of variables, based on a Bayesian TVP-VAR (based on one lag).
Figure 9:

Posterior means of coefficients (alternative model VI). Posterior means of coefficients of variables, based on a Bayesian TVP-VAR (based on one lag).

References

Ahir, H., N. Bloom, and D. Furceri. 2022. The World Uncertainty Index. Technical Report. National Bureau of Economic Research. Also available at: https://www.nber.org/system/files/working_papers/w29763/w29763.pdf.10.3386/w29763Search in Google Scholar

Alquist, R., and L. Kilian. 2010. “What Do We Learn from the Price of Crude Oil Futures?” Journal of Applied Econometrics 25 (4): 539–73. https://doi.org/10.1002/jae.1159.Search in Google Scholar

An, S., and H. Kang. 2011. “Oil Shocks in a DSGE Model for the Korean Economy.” In ‘Commodity Prices and Markets, East Asia Seminar on Economics, Volume 20’, NBER Chapters, 295–321. National Bureau of Economic Research, Inc.10.7208/chicago/9780226386904.003.0010Search in Google Scholar

Antonakakis, N., I. Chatziantoniou, and G. Filis. 2013. “Dynamic Co-movements of Stock Market Returns, Implied Volatility and Policy Uncertainty.” Economics Letters 120 (1): 87–92. https://doi.org/10.1016/j.econlet.2013.04.004.Search in Google Scholar

Antonakakis, N., I. Chatziantoniou, and G. Filis. 2014. “Dynamic Spillovers of Oil Price Shocks and Economic Policy Uncertainty.” Energy Economics 44: 433–47. https://doi.org/10.1016/j.eneco.2014.05.007.Search in Google Scholar

Arampatzidis, I., T. Dergiades, R. Kaufmann, and T. Panagiotidis. 2021. “Oil and the US Stock Market: Implications for Low Carbon Policies.” Energy Economics 103: 105588. https://doi.org/10.1016/j.eneco.2021.105588.Search in Google Scholar

Arouri, M., C. Rault, and F. Teulon. 2014. “Economic Policy Uncertainty, Oil Price Shocks and GCC Stock Markets.” Economics Bulletin 34 (3): 1822–34. https://doi.org/10.1016/j.resourpol.2023.104082.Search in Google Scholar

Auerbach, A., and Y. Gorodnichenko. 2012. “Measuring the Output Responses to Fiscal Policy.” American Economic Journal: Economic Policy 4 (2): 1–27. https://doi.org/10.1257/pol.5.3.320.Search in Google Scholar

Auerbach, A., and Y. Gorodnichenko. 2013. “Fiscal Multipliers in Recession and Expansion.” NBER.10.3386/w19911Search in Google Scholar

Baker, S., N. Bloom, and S. Davis. 2016. “Measuring Economic Policy Uncertainty.” Quarterly Journal of Economics 131 (4): 1593–636. https://doi.org/10.1093/qje/qjw024.Search in Google Scholar

Bernanke, B. 2004. The Great Moderation. Washington. Also available at: https://www.federalreserve.gov/boarddocs/speeches/2004/20040220/.Search in Google Scholar

Bloom, N. 2009. “The Impact of Uncertainty Shocks.” Econometrica 77 (3): 623–85. https://doi.org/10.3982/ECTA6248.Search in Google Scholar

Bloom, N., S. Bond, and J. Van Reenen. 2007. “Uncertainty and Investment Dynamics.” The Review of Economic Studies 74 (2): 391–415. https://doi.org/10.1111/j.1467-937X.2007.00426.x.Search in Google Scholar

Bodenstein, M., C. Erceg, and L. Guerrieri. 2008. “Optimal Monetary Policy with Distinct Core and Headline Inflation Rates.” Journal of Monetary Economics 55: S18–33. https://doi.org/10.1016/j.jmoneco.2008.07.010.Search in Google Scholar

Bodenstein, M., C. Erceg, and L. Guerrieri. 2011. “Oil Shocks and External Adjustment.” Journal of International Economics 83 (2): 168–84. https://doi.org/10.1016/j.jinteco.2010.10.006.Search in Google Scholar

Breheny, P., and W. Burchett. 2017. “Visualization of Regression Models Using Visreg.” R Journal 9 (2): 56. https://doi.org/10.32614/rj-2017-046.Search in Google Scholar

Caggiano, G., E. Castelnuovo, and N. Groshenny. 2014. “Uncertainty Shocks and Unemployment Dynamics in US Recessions.” Journal of Monetary Economics 67: 78–92. https://doi.org/10.1016/j.jmoneco.2014.07.006.Search in Google Scholar

Caggiano, G., E. Castelnuovo, and J. Figueres. 2017. “Economic Policy Uncertainty and Unemployment in the United States: A Nonlinear Approach.” Economics Letters 151: 31–4. https://doi.org/10.1016/j.econlet.2016.12.002.Search in Google Scholar

Degiannakis, S., G. Filis, and R. Kizys. 2014. “The Effects of Oil Price Shocks on Stock Market Volatility: Evidence from European Data.” Energy Journal 35 (1): 35–56, https://doi.org/10.5547/01956574.35.1.3.Search in Google Scholar

Degiannakis, S., G. Filis, and S. Panagiotakopoulou. 2018. “Oil Price Shocks and Uncertainty: How Stable Is Their Relationship over Time?” Economic Modelling 72: 42–53. https://doi.org/10.1016/j.econmod.2018.01.004.Search in Google Scholar

Eilers, P., and B. Marx. 1996. “Flexible Smoothing with B-Splines and Penalties.” Statistical Science 11 (2): 89–102. https://doi.org/10.1214/ss/1038425655.Search in Google Scholar

Fernández, A., S. Schmitt-Grohé, and M. Uribe. 2017. “World Shocks, World Prices, and Business Cycles: An Empirical Investigation.” Journal of International Economics 108: S2–14. https://doi.org/10.1016/j.jinteco.2017.01.001.Search in Google Scholar

Foroni, C., P. Guérin, and M. Marcellino. 2017. “Explaining the Time-Varying Effects of Oil Market Shocks on US Stock Returns.” Economics Letters 155: 84–8. https://doi.org/10.1016/j.econlet.2017.03.017.Search in Google Scholar

Galí, J., and L. Gambetti. 2009. “On the Sources of the Great Moderation.” American Economic Journal: Macroeconomics 1 (1): 26–57. https://doi.org/10.1257/mac.1.1.26.Search in Google Scholar

Gbohoui, W. 2021. “Uncertainty and Public Investment Multipliers: The Role of Economic Confidence.” IMF Working Papers, Vol. 2021. Also available at: https://www.imf.org/en/Publications/WP/Issues/2021/11/12/Uncertainty-and-Public-Investment-Multipliers-The-Role-of-Economic-Confidence-506825.10.5089/9781616356200.001Search in Google Scholar

Ginn, W. 2022. “Climate Disasters and the Macroeconomy: Does State-Dependence Matter? Evidence for the US.” Economics of Disasters and Climate Change 6: 141–61. https://doi.org/10.1007/s41885-021-00102-6.Search in Google Scholar

Ginn, W. 2023. “World Output and Commodity Price Cycles.” International Economic Journal 37: 1–25, https://doi.org/10.1080/10168737.2023.2263844.Search in Google Scholar

Güntner, J. H., and K. Linsbauer. 2018. “The Effects of Oil Supply and Demand Shocks on US Consumer Sentiment.” Journal of Money, Credit, and Banking 50 (7): 1617–44. https://doi.org/10.1111/jmcb.12512.Search in Google Scholar

Hailemariam, A., R. Smyth, and X. Zhang. 2019. “Oil Prices and Economic Policy Uncertainty: Evidence from a Nonparametric Panel Data Model.” Energy Economics 83: 40–51. https://doi.org/10.1016/j.eneco.2019.06.010.Search in Google Scholar

Hamilton, J. 1983. “Oil and the Macroeconomy since World War II.” Journal of Political Economy 91 (2): 228–48. https://doi.org/10.1086/261140.Search in Google Scholar

Hamilton, J. 2008. “Oil and the Macroeconomy.” The New Palgrave Dictionary of Economics 2: 35–56, https://doi.org/10.1057/978-1-349-95121-5_2119-1.Search in Google Scholar

Handley, K., and N. Limao. 2015. “Trade and Investment under Policy Uncertainty: Theory and Firm Evidence.” American Economic Journal: Economic Policy 7 (4): 189–222. https://doi.org/10.1257/pol.20140068.Search in Google Scholar

He, Z., and J. Niu. 2018. “The Effect of Economic Policy Uncertainty on Bank Valuations.” Applied Economics Letters 25 (5): 345–7. https://doi.org/10.1080/13504851.2017.1321832.Search in Google Scholar

Hodrick, R., and E. Prescott. 1997. “Postwar US Business Cycles: An Empirical Investigation.” Journal of Money, Credit, and Banking 29: 1–16, https://doi.org/10.2307/2953682.Search in Google Scholar

Jacks, D., K. O’rourke, and J. Williamson. 2011. “Commodity Price Volatility and World Market Integration since 1700.” The Review of Economics and Statistics 93 (3): 800–13. https://doi.org/10.1162/REST_a_00091.Search in Google Scholar

Jordà, Ò.. 2005. “Estimation and Inference of Impulse Responses by Local Projections.” The American Economic Review 95 (1): 161–82. https://doi.org/10.1257/0002828053828518.Search in Google Scholar

Kang, W., and R. Ratti. 2013a. “Oil Shocks, Policy Uncertainty and Stock Market Return.” Journal of International Financial Markets, Institutions and Money 26: 305–18. https://doi.org/10.1016/j.intfin.2013.07.001.Search in Google Scholar

Kang, W., and R. Ratti. 2013b. “Structural Oil Price Shocks and Policy Uncertainty.” Economic Modelling 35: 314–9. https://doi.org/10.1016/j.econmod.2013.07.025.Search in Google Scholar

Kang, W., K. Lee, and R. Ratti. 2014. “Economic Policy Uncertainty and Firm-Level Investment.” Journal of Macroeconomics 39: 42–53. https://doi.org/10.1016/j.jmacro.2013.10.006.Search in Google Scholar

Kang, W., R. Ratti, and J. Vespignani. 2017. “Oil Price Shocks and Policy Uncertainty: New Evidence on the Effects of US and Non-US Oil Production.” Energy Economics 66: 536–46. https://doi.org/10.1016/j.eneco.2017.01.027.Search in Google Scholar

Kilian, L. 2009. “Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market.” The American Economic Review 99 (3): 1053–69. https://doi.org/10.1257/aer.99.3.1053.Search in Google Scholar

Kilian, L. 2010. Oil Price Volatility: Origins and Effects. Technical Report. WTO Staff Working Paper.Search in Google Scholar

Kilian, L. 2014. “Oil Price Shocks: Causes and Consequences.” Annual Reviews 6: 133–54, https://doi.org/10.1146/annurev-resource-083013-114701.Search in Google Scholar

Kilian, L., and D. Murphy. 2012. “Why Agnostic Sign Restrictions Are Not Enough: Understanding the Dynamics of Oil Market VAR Models.” Journal of the European Economic Association 10 (5): 1166–88. https://doi.org/10.1111/j.1542-4774.2012.01080.x.Search in Google Scholar

Kilian, L., and R. Vigfusson. 2017. “The Role of Oil Price Shocks in Causing US Recessions.” Journal of Money, Credit, and Banking 49 (8): 1747–76. https://doi.org/10.1111/jmcb.12430.Search in Google Scholar

Kilian, L., and X. Zhou. 2018. “Modeling Fluctuations in the Global Demand for Commodities.” Journal of International Money and Finance 88: 54–78. https://doi.org/10.1016/j.jimonfin.2018.07.001.Search in Google Scholar

Koop, G., and D. Korobilis. 2010. “Bayesian Multivariate Time Series Methods for Empirical Macroeconomics.” Foundations and Trends in Econometrics 3 (4): 267–358. https://doi.org/10.1561/0800000013.Search in Google Scholar

Li, D., J. Chen, and J. Gao. 2011. “Non-parametric Time-Varying Coefficient Panel Data Models with Fixed Effects.” The Econometrics Journal 14 (3): 387–408. https://doi.org/10.1111/j.1368-423X.2011.00350.x.Search in Google Scholar

López-Villavicencio, A., and M. Pourroy. 2019. “Inflation Target and (A)symmetries in the Oil Price Pass-Through to Inflation.” Energy Economics 80: 860–75. https://doi.org/10.1016/j.eneco.2019.01.025.Search in Google Scholar

Medina, J. P., and C. Soto. 2005. “Oil Shocks and Monetary Policy in an Estimated DSGE Model for a Small Open Economy.” Working Papers Central Bank of Chile 353. Central Bank of Chile. Also available at: https://ideas.repec.org/p/chb/bcchwp/353.html.Search in Google Scholar

Ozturk, E. O., and X. S. Sheng. 2018. “Measuring Global and Country-specific Uncertainty.” Journal of International Money and Finance 88: 276–95. https://doi.org/10.1016/j.jimonfin.2017.07.014.Search in Google Scholar

Ramey, V., and S. Zubairy. 2018. “Government Spending Multipliers in Good Times and in Bad: Evidence from US Historical Data.” Journal of Political Economy 126 (2): 850–901. https://doi.org/10.1086/696277.Search in Google Scholar

Ravn, M., and H. Uhlig. 2002. “On Adjusting the Hodrick-Prescott Filter for the Frequency of Observations.” The Review of Economics and Statistics 84 (2): 371–6. https://doi.org/10.2139/ssrn.273355.Search in Google Scholar

Sheng, X., R. Gupta, and Q. Ji. 2020. “The Impacts of Structural Oil Shocks on Macroeconomic Uncertainty: Evidence from a Large Panel of 45 Countries.” Energy Economics 91: 104940. https://doi.org/10.1016/j.eneco.2020.104940.Search in Google Scholar

Wood, S., F. Scheipl, and J. Faraway. 2013. “Straightforward Intermediate Rank Tensor Product Smoothing in Mixed Models.” Statistics and Computing 23 (3): 341–60. https://doi.org/10.1007/s11222-012-9314-z.Search in Google Scholar

You, W., Y. Guo, H. Zhu, and Y. Tang. 2017. “Oil Price Shocks, Economic Policy Uncertainty and Industry Stock Returns in China: Asymmetric Effects with Quantile Regression.” Energy Economics 68: 1–18. https://doi.org/10.1016/j.eneco.2017.09.007.Search in Google Scholar

Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/snde-2023-0018).

Received: 2023-03-04
Accepted: 2024-03-10
Published Online: 2024-04-10

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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  2. Research Articles
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https://doi.org/10.1515/snde-2023-0018
Keywords for this article
oil prices; business cycles; world economic uncertainty

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Multiscale SUR Estimation of Systematic Risk
  4. A Simulation and Empirical Study of the Maximum Likelihood Estimator for Stochastic Volatility Jump-Diffusion Models
  5. Core Inflation Rate for China and the ASEAN-10 Countries: Smoothed Signal for Score-Driven Local Level Plus Scale Models
  6. Diversified Reward-Risk Parity in Portfolio Construction
  7. Time-Varying Parameter Four-Equation DSGE Model
  8. Does State Dependence Matter in Relation to Oil Price Shocks on Global Economic Conditions?
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