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ASYMMETRIES IN BUSINESS CYCLES AND THE ROLE OF OIL PRICES

Published online by Cambridge University Press:  27 July 2017

Betty C. Daniel*
Affiliation:
The University at Albany
Christian M. Hafner
Affiliation:
Université catholique de Louvain
Léopold Simar
Affiliation:
Université catholique de Louvain
Hans Manner
Affiliation:
University of Graz
*
Address correspondence to: Betty C. Daniel, Department of Economics, The University at Albany, 1400 Washington Ave, Albany, NY 12222, USA; e-mail: bdaniel@albany.edu.

Abstract

We estimate asymmetries in innovations to Solow residuals for 11 Organization for Economic Co-operation and Development (OECD) countries using stochastic frontier analysis. Likelihood ratio statistics and variance ratios imply that all countries with net energy imports have significant negative asymmetries, whereas other countries do not. We construct a simple theoretical model in which the measured Solow residual combines effects from technology, factor utilization, and the terms of trade. For oil importers, the model implies an asymmetric response of measured total factor productivity to oil price increases and decreases. When we condition Solow residuals separately on positive and negative oil price changes to allow asymmetric responses, evidence for remaining negative asymmetric innovations to the Solow residuals vanishes for all countries except Switzerland. Switzerland's relatively dominant financial sector suggests that their asymmetries could be due to a financial crisis, a hypothesis that we test and fail to reject.

Type
Articles
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

We would like to thank the associate editor, two anonymous referees, Jonas Dovern, Stephie Fried, and the participants in the European Workshop on Efficiency and Productivity in Verona for helpful comments and suggestions.

References

REFERENCES

Aigner, D. J., Lovel, C. A. K., and Schmidt, P. J. (1977) Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 6, 2137.Google Scholar
Barro, R. (2006) Rare disasters and asset markets in the twentieth century. Quarterly Journal of Economics 121, 823866.Google Scholar
Basu, S. and Kimball, M. S. (1997) Cyclical Productivity with Unobserved Input Variation. NBER working paper 5915.Google Scholar
Battese, G. E. and Coelli, T. J. (1988) Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data. Journal of Econometrics 38, 387399.Google Scholar
Bolton, P. (2010) Energy Imports and Exports. Commons library standard note – UK parliament.Google Scholar
Burnside, C., Eichenbaum, M., and Rebelo, S. (1995) Capital utilization and returns to scale. In Bernanke, B. S. and Rotemberg, J. J. (eds.), NBER Macroeconomics Annual 1995, vol. 10, pp. 67124. Cambridge, MA: MIT Press.Google Scholar
Clements, M. and Krolzig, H. M. (2002) Can oil shocks explain asymmetries in the US business cycle? Empirical Economics 27 (2), 185204.Google Scholar
Easterly, W. and Levine, R. (2001) It's not factor accumulation: stylized facts and growth models. World Bank Economic Review 15, 177219.Google Scholar
Engemann, K. M., Kliesen, K. L., and Owyang, M. T. (2011) Do oil shocks drive business cycles? Some U.S. and international evidence. Macroeconomic Dynamics 15, 498517.Google Scholar
Hafner, C., Manner, H., and Simar, L. (2016) The “wrong skewness'' problem in stochastic frontier models: A new approach. Econometrie Reviews (forthcoming).Google Scholar
Hall, R. (1988) The relation between price and marginal cost in the U.S. industry. Journal of Political Economy 96, 921947.Google Scholar
Hamilton, J. D. (1983) Oil and the macroeconomy since World War II. Journal of Political Economy 91, 228248.Google Scholar
Hamilton, J. D. (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57, 357384.Google Scholar
Hamilton, J. D. (1996) This is what happened to the oil price/macroeconomy relation. Journal of Monetary Economics 38, 215220.Google Scholar
Hamilton, J. D. (2003) What is an oil shock? Journal of Econometrics 113 (2), 363398.Google Scholar
Hamilton, J. D. (2011) Nonlinearities and the macroeconomic effects of oil prices. Macroeconomic Dynamics 15 (S3), 364378.Google Scholar
Hassler, J., Krusell, P., and Olovsson, C. (2012) Energy-Saving Technical Change. NBER working paper 18456.Google Scholar
Kehoe, T. J. and Ruhl, K. J. (2008) Are shocks to the terms of trade shocks to productivity? Review of Economic Dynamics 11, 804819.Google Scholar
Kilian, L. (2008) A comparison of the effects of exogenous oil supply shocks on output and inflation in the G7 countries. Journal of the European Economic Association 6 (1), 78121.Google Scholar
Kilian, L. and Vigfusson, R. J. (2011a) Are the responses of the U.S. economy asymmetric in energy price increases and decreases? Quantitative Economics 2, 419453.Google Scholar
Kilian, L. and Vigfusson, R. J. (2011b) Nonlinearities in the oil price-output relationship. Macroeconomic Dynamics 15 (S3), 337363.Google Scholar
Kumbhakar, S. C. and Lovell, C. A. K. (2000) Stochastic Frontier Analysis. Cambridge, England: Cambridge University Press.Google Scholar
Lee, L. F. (1993) Asymptotic distribution of the maximum likelihood estimator for a stochastic frontier function model with a singluar information matrix. Econometric Theory 9, 413430.Google Scholar
McKay, A. and Reis, R. (2008) The brevity and violence of contractions and expansions. Journal of Monetary Economics 55 (4), 738751.Google Scholar
Meeusen, W. and van den Broek, J. (1977) Efficiency estimation from Cobb-Douglas production functions with composed errors. International Economic Review 18, 435444.Google Scholar
Mork, K. A. (1989) Oil and the macroeconomy when prices go up and down: An extension of Hamilton's results. Journal of Political Economy 97 (3), 740744.Google Scholar
Murillo-Zamorano, L. R. (2004) Economic efficiency and frontier techniques. Journal of Economic Surveys 18, 3377.Google Scholar
Prescott, E. C. (1986) Theory ahead of business-cycle measurement. Carnegie-Rochester Conference Series on Public Policy 25, 1144.Google Scholar
Ramey, V. A. (2016) Macroeconomic Shocks and Their Propagation. NBER working paper 21978.Google Scholar
Raymond, J. E. and Rich, R. W. (1997) Oil and the macroeconomy: A Markov state-switching approach. Journal of Money, Credit and Banking 29, 193213.Google Scholar
Shapiro, M. D. (1993) Cyclical productivity and the workweek of capital. American Economic Review 83 (2), 229233.Google Scholar
Stock, J. H. and Watson, M. W. (1999) Business cycle fluctuations in us macroeconomic time series. In Taylor, J. B. and Woodford, M. (eds.), Handbook of Macroeconomics, vol. 1, pp. 364. Amsterdam: North-Holland Publishing.Google Scholar
Whelan, K. (2002) A guide to the use of chain aggregated NIPA data. Review of Income and Wealth 48 (2), 217233.Google Scholar