Hostname: page-component-8448b6f56d-wq2xx Total loading time: 0 Render date: 2024-04-23T09:14:37.393Z Has data issue: false hasContentIssue false
  • English
  • Français

IS BUSINESS CYCLE ASYMMETRY INTRINSIC IN INDUSTRIALIZED ECONOMIES?

Published online by Cambridge University Press:  24 January 2019

James Morley  and
Irina B. Panovska
James Morley
Affiliation:
University of Sydney
Irina B. Panovska*
Affiliation:
Lehigh University
*
Address correspondence to: Irina B. Panovska, Department of Economics, Rauch Business Center #457, Lehigh University, 621 Taylor Street, Bethlehem, PA, USA. e-mail: irp213@lehigh.edu. Phone: (610) 758–1068.
Get access Rights & Permissions [Opens in a new window]

Abstract

We consider a model-averaged forecast-based estimate of the output gap to measure economic slack in 10 industrialized economies. Our measure takes changes in the long-run growth rate into account and, by addressing model uncertainty using equal weights on different forecast-based estimates, is robust to different assumptions about the underlying structure of the economy. For all 10 countries in the sample, we find that the estimated output gap has much larger negative movements during recessions than positive movements in expansions, suggesting business cycle asymmetry is an intrinsic characteristic of industrialized economies. Furthermore, the estimated output gap is always strongly negatively correlated with future output growth and unemployment and positively correlated with capacity utilization. It also implies a convex Phillips Curve in many cases. The model-averaged output gap is reliable in real time in the sense of being subject to relatively small revisions.

Keywords

Output GapModel AveragingMarkov SwitchingBusiness Cycle AsymmetryConvex Phillips Curve
Type
Articles
Information
Macroeconomic Dynamics , Volume 24 , Issue 6 , September 2020 , pp. 1403 - 1436
Copyright
© 2019 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

An earlier version of this study that focused on Asia-Pacific economies circulated under the title of “Measuring Economic Slack: A Forecast-Based Approach with Applications to Economies in Asia and the Pacific.” We thank the associate editor and two anonymous referees for helpful comments and suggestions. We also thank Stephane Dees, Jun Il Kim, Aaron Mehrotra, Tim Robinson, James Yetman, and Alex Nikolsko-Rzhevskyy, as well as conference and seminar participants at the 2018 Conference of the International Association for Applied Econometrics, 2017 Symposium of the Society for Nonlinear Dynamics and Econometrics, the Bureau of Economic Analysis, Lafayette College, the University of Wisconsin Whitewater, People’s Bank of China-BIS Conference on “Globalisation and Inflation Dynamics in Asia and the Pacific,” the “Continuing Education in Macroeconometrics” workshop at the University of New South Wales, the Sydney Macroeconomics Readings Group, the European Central Bank, and the University of Technology Sydney for helpful questions and comments. The usual disclaimers apply.

References

Auroba, S. B., Bocola, L. and Schorfheide, F. (2017) Assessing DSGE model nonlinearities. Journal of Economic Dynamics and Control 83, 127–142.Google Scholar
Bai, J. and Perron, P. (1998) Estimating and testing linear models with multiple structural changes. Econometrica 66, 47–78.10.2307/2998540CrossRefGoogle Scholar
Bai, J. and Perron, P. (2003) Computation and analysis of multiple structural change models. Journal of Applied Econometrics 18, 1–22.CrossRefGoogle Scholar
Barnichnon, R. (2010) Productivity and unemployment over the business cycle. Journal of Monetary Economics 57, 1013–1025.10.1016/j.jmoneco.2010.09.006CrossRefGoogle Scholar
Basistha, A. and Nelson, C. R. (2007) New measure of the output gap based on the forward-looking new Keynesian Phillips curve. Journal of Monetary Economics 54, 498–511.CrossRefGoogle Scholar
Bates, J.M. and Granger, C.W. J. (1969) The combination of forecasts. Operations Research Quarterly 20, 451–468.10.1057/jors.1969.103CrossRefGoogle Scholar
Berger, T., Everaet, G. and Vierke, H. (2016) Testing for time variation in an unobserved components model for the US economy. Journal of Economic Dynamics and Control 69, 179–208.10.1016/j.jedc.2016.05.017CrossRefGoogle Scholar
Beveridge, S. and Nelson, C. R. (1981) A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the business cycle. Journal of Monetary Economics 7, 151–174.10.1016/0304-3932(81)90040-4CrossRefGoogle Scholar
Canova, F. (1998) Detrending and business cycle facts. Journal of Monetary Economics 41, 475–512.10.1016/S0304-3932(98)00006-3CrossRefGoogle Scholar
Carrasco, M., Hu, L. and Ploberger, W. (2014) Optimal test for Markov switching parameters. Econometrica 82, 765–784.Google Scholar
Clark, P. K. (1987) The cycle component of the U.S. economic activity. Quarterly Journal of Economics 102, 797–814.10.2307/1884282CrossRefGoogle Scholar
Cogley, T. and Nason, J. M. (1995) Effects of the Hodrick-Prescott filter on trend and difference stationary time series: Implications for business cycle research. Journal of Economic Dynamics and Control 19, 253–278.10.1016/0165-1889(93)00781-XCrossRefGoogle Scholar
Coibion, O., Gorodnichenko, Y. and Ulate, M. (2017) The Cyclical Sensitivity in Estimates of Potential Output. National Bureau of Economic Research, Inc., NBER Working Paper: No. 23580.CrossRefGoogle Scholar
Davidson, R. and MacKinnon, J. G. (2004) Econometric Theory and Methods. New York: Oxford University Press.Google Scholar
De Jong, R. M. and Sakarya, N. (2016) The econometrics of the Hodrick-Prescott filter. Review of Economics and Statistics 98, 310–317.10.1162/REST_a_00523CrossRefGoogle Scholar
Dickey, D. A. and Fuller, W. A. (1979) Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74, 427–431.Google Scholar
Diebold, F. X., Schorfheide, F. and Shin, M. (2017) Real-time forecast evaluation of DSGE models with stochastic volatility. Journal of Econometrics 201, 322–332.10.1016/j.jeconom.2017.08.011CrossRefGoogle Scholar
Dupasquier, C. and Ricketts, N. (1998) Non-linearities in the Output-Inflation Relationship: Some Empirical Results for Canada. Bank of Canada Working Paper 98–14.Google Scholar
Elliott, G., Rothenberg, T. J. and Stock, J. H. (1996) Efficient tests for an autoregressive unit root. Econometrica 64, 813–836.10.2307/2171846CrossRefGoogle Scholar
Fernandez-Villaverde, J., Rubio-Ramirez, J. F., Sargent, T. J. and Watson, M.W. (2007) ABCs (and Ds) of understanding VARs. American Economic Review 97(3), 1021–1026.10.1257/aer.97.3.1021CrossRefGoogle Scholar
Garratt, A., Mitchell, J. and Vahey, S. P. (2014) Measuring output gap nowcast uncertainty. International Journal of Forecasting 30, 268–279.10.1016/j.ijforecast.2013.07.012CrossRefGoogle Scholar
Geweke, J. and Amisano, G. (2011) Optimal prediction pools. Journal of Econometrics 164, 130–141.CrossRefGoogle Scholar
Gonzalez-Astudillo, M. (2017) GDP Trend-cycle Decompositions Using State-Level Data. Federal Reserve Board Working Paper 2017–051.10.17016/FEDS.2017.051CrossRefGoogle Scholar
Gonzalez-Astudillo, M. and Roberts, J. (2018) When Can Trend Cycle Decompositions Be Trusted? Federal Reserve Board Working Paper 2016–099.10.17016/FEDS.2016.099CrossRefGoogle Scholar
Guerrieri, L. and Iacoviello, M. (2017) Collateral constraints and macroeconomic asymmetries. Journal of Monetary Economics 90, 28–49.10.1016/j.jmoneco.2017.06.004CrossRefGoogle Scholar
Hamilton, J. D. (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57, 357–384.10.2307/1912559CrossRefGoogle Scholar
Hamilton, J. D.(Forthcoming) Why you should never use the Hodrick-Prescott filter. Review of Economics and Statistics.Google Scholar
Harvey, A. C. and Jaeger, A. (1993) Detrending, stylized facts and the business cycle. Journal of Applied Econometrics 8, 231–247.CrossRefGoogle Scholar
Harvey, A. C., Trimbur, T. M. and Van Dijk, H. K. (2007) Trends and cycles in economic time series: A Bayesian approach. Journal of Econometrics 140, 618–649.10.1016/j.jeconom.2006.07.006CrossRefGoogle Scholar
Hodrick, R. J. and Prescott, E. C. (1997) Postwar US business cycles: An empirical investigation. Journal of Money, Credit, and Banking 29, 1–16.CrossRefGoogle Scholar
Huang, Y.-F., Luo, S. and Startz, R. (2016) Are Recoveries All the Same: GDP and TFP? University of California Santa Barbara Working Paper.CrossRefGoogle Scholar
IMF. (2013) World Economic Outlook “Hopes, Realities, Risks” Chapter 3. Washington, DC: International Monetary Fund.Google Scholar
Kamber, G., Morley, J. and Wong, B. (2018) Intuitive and reliable estimates of the output gap from a Beveridge-Nelson filter. Review of Economics and Statistics 100(3), 50–566.10.1162/rest_a_00691CrossRefGoogle Scholar
Kiley, M. T. (2013) Output gaps. Journal of Macroeconomics 37, 1–18.10.1016/j.jmacro.2013.04.002CrossRefGoogle Scholar
Kim, C.-J. (1994) Dynamic linear models with Markov switching. Journal of Econometrics 60, 1–22.10.1016/0304-4076(94)90036-1CrossRefGoogle Scholar
Kim, C.-J., Morley, J. and Piger, J. (2005) Nonlinearity and the permanent effects of recessions. Journal of Applied Econometrics 20, 291–309.10.1002/jae.831CrossRefGoogle Scholar
Kim, C.-J. andNelson, C. R. (1999) Friedman’s Plucking model of business fluctuations: Tests and estimates of permanent and transitory components. Journal of Money, Credit and Banking 31, 317–334.CrossRefGoogle Scholar
Klinger, S. and Weber, E. (2016) Detecting unemployment hysteresis: A simultaneous unobserved components model with Markov switching. Economics Letters 114, 115–118.10.1016/j.econlet.2016.04.027CrossRefGoogle Scholar
Kuttner, K. N. (1994) Estimating potential output as a latent variable. Journal of Business & Economic Statistics 12, 361–368.Google Scholar
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P. and Shin, Y. (1992) Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics 54, 159–178.10.1016/0304-4076(92)90104-YCrossRefGoogle Scholar
Levin, A. T. and Piger, J. M. (2006) Is Inflation Persistence Intrinsic in Industrial Economies? University of Oregon Working Paper.Google Scholar
Leybourne, S. J. and McCabe, B. P. M. (1992) Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics 64, 159–178.Google Scholar
Lucas, R. E. (1976) Economic policy evaluation: A critique. Carnegie-Rochester Conference Series on Public Policy 1, 19–46.CrossRefGoogle Scholar
Meier, A. (2010) Still minding the gap—Inflation dynamics during episodes of persistent large output gaps. International Monetary Fund, IMF Working Papers 10/189.CrossRefGoogle Scholar
Morley, J. (2014) Measuring economic slack: A forecast-based approach with applications to economies in Asia and the Pacific. BIS Working Paper, No. 451.Google Scholar
Morley, J., Nelson, C. R. and Zivot, E. (2003) Why are the Beveridge-Nelson and unobservedcomponents decompositions of GDP so different? Review of Economics and Statistics 85, 235–243.10.1162/003465303765299765CrossRefGoogle Scholar
Morley, J., Panovska, I. B. and Sinclair, T. M. (2017) Testing stationarity with unobserved components models. Macroeconomic Dynamics 21, 160–182.10.1017/S1365100515000437CrossRefGoogle Scholar
Morley, J. and Piger, J. (2008) Trend/cycle decomposition of regime-switching processes. Journal of Econometrics 146, 220–226.10.1016/j.jeconom.2008.08.009CrossRefGoogle Scholar
Morley, J. and Piger, J. (2012) The asymmetric business cycle. Review of Economics and Statistics 94, 208–221.10.1162/REST_a_00169CrossRefGoogle Scholar
Murray, C. J. (2003) Cyclical properties of Baxter-King filtered time series. Review of Economics and Statistics 85, 472–476.CrossRefGoogle Scholar
Nelson, C. R. (2008) The Beveridge-Nelson decomposition in retrospect and prospect. Journal of Econometrics 146, 202–206.10.1016/j.jeconom.2008.08.008CrossRefGoogle Scholar
Nelson, C. R. and Kang, H. (1981) Spurious periodicity in inappropriately detrended time series. Econometrica 49, 741–751.10.2307/1911520CrossRefGoogle Scholar
Nikolsko-Rzhevskyy, A. (2011) Monetary policy estimation in real time: Forward-looking Taylor rules without forward-looking data. Journal of Money, Credit, and Banking 43, 871–897.10.1111/j.1538-4616.2011.00400.xCrossRefGoogle Scholar
Orphanides, A. (2002) Monetary-policy rules and the great inflation. American Economic Review 92, 115–120.CrossRefGoogle Scholar
Orphanides, A. and van Norden, S. (2002) The unreliability of output-gap estimates in real time. Review of Economics and Statistics 84, 569–583.10.1162/003465302760556422CrossRefGoogle Scholar
Panovska, I. B. (2017) What explains the recent jobless recoveries? Macroeconomic Dynamics 21(3), 808–832.CrossRefGoogle Scholar
Perron, P. and Wada, T. (2009) Let’s take a break: Trends and cycles in U.S. real GDP. Journal of Monetary Economics 56, 749–765.10.1016/j.jmoneco.2009.08.001CrossRefGoogle Scholar
Perron, P. and Wada, T. (2016) Measuring business cycles with structural breaks and outliers: Applications to international data. Research in Economics 70, 281–303.10.1016/j.rie.2015.12.001CrossRefGoogle Scholar
Sims, C. A. (1980) Macroeconomics and reality. Econometrica 48, 1–48.CrossRefGoogle Scholar
Sinclair, T. M. (2009) The relationships between permanent and transitory movements in U.S. output and the unemployment rate. Journal of Money, Credit, and Banking 41, 592–542.10.1111/j.1538-4616.2009.00220.xCrossRefGoogle Scholar
Sinclair, T. M. (2010) Asymmetry in the business cycle: Friedman’s plucking model with correlated innovations. Studies in Nonlinear Dynamics and Econometrics 14(1), Article 3. doi: 10.2202/1558-3708.1688.Google Scholar
Stock, J. H. and Watson, M. W. (2009) Phillips curve inflation forecasts. In: Understanding Inflation and the Implications for Monetary Policy, a Phillips Curve Retrospective, eds Fuhrer, J, Kodrzycki, Y, Little, J, Olivei, G. Cambridge, MA: MIT Press pp. 99–202.Google Scholar
Stock, J. H. and Watson, M. W. (2010) Modeling inflation after the crisis. In: Macroeconomic Challenges: The Decade Ahead, Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pp. 173–220. Kansas City, MO: Federal Reserve Bank of Kansas City.Google Scholar
Yellen, J. L. (2015) The Economic Outlook and Monetary Policy. Speech at the Economic Club of Washington, D.C. December 2nd, 2015. http://www.federalreserve.gov/newsevents/speech/yellen20151202a.htm.Google Scholar
Supplementary material: PDF

Morley and Panovska supplementary material

Appendix

Download Morley and Panovska supplementary material(PDF)
PDF 428.2 KB