Hostname: page-component-76fb5796d-dfsvx Total loading time: 0 Render date: 2024-04-27T04:50:31.942Z Has data issue: false hasContentIssue false
  • English
  • Français

REGIME-SWITCHING PRODUCTIVITY GROWTH AND BAYESIAN LEARNING IN REAL BUSINESS CYCLES

Published online by Cambridge University Press:  14 May 2019

Sami Alpanda
Sami Alpanda*
Affiliation:
University of Central Florida
*
Address correspondence to: University of Central Florida, Department of Economics, College of Business Administration, 4336 Scorpius Street, Orlando, FL 32816, USA. Phone: (407) 823-1575. e-mail: sami.alpanda@ucf.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

Growth in total factor productivity (TFP) in the USA has slowed down significantly since the mid-2000s, reminiscent of the productivity slowdown of the 1970s. This paper investigates the implications of a productivity slowdown on macroeconomic variables using a standard real business cycle (RBC) model, extended with regime-switching in trend productivity growth and Bayesian learning regarding the growth regime. I estimate the Markov-switching parameters using US data and maximum-likelihood methods, and compute the model solution using global projection methods. Simulations reveal that, while adding a regime-switching component to the standard RBC setup increases the volatility in the system, further incorporating incomplete information and learning significantly dampens this effect. The dampening is mainly due to the responses of investment and labor in response to a switch in the trend component of TFP growth, which are weaker in the incomplete information case as agents mistakenly place some probability that the observed decline in TFP growth is due to the transient component and not due to a regime switch. The model offers an objective way to infer slowdowns in trend productivity, and suggests that macroeconomic aggregates in the USA are currently close to their potential levels given observed productivity, while counterfactual simulations indicate that the cost of the productivity slowdown to US welfare has been significant.

Keywords

Productivity SlowdownRegime-Switching TFP GrowthReal Business CyclesBayesian Learning
Type
Articles
Information
Macroeconomic Dynamics , Volume 25 , Issue 2 , March 2021 , pp. 462 - 488
Copyright
© Cambridge University Press 2019

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

I thank the editor William A. Barnett, two anonymous referees, Ben Bridgeman, Gino Cateau, Andrew Foerster, Ellen McGrattan, Adrian Peralta-Alva, Edward Prescott, Tatevik Sekhposyan, Alexander Ueberfeldt, and Geoffrey Woglom for suggestions and comments. All remaining errors are my own.

References

REFERENCES

Alpanda, S. and Peralta-Alva, A. (2010) Oil crisis, energy-saving technological change and the stock market crash of 1973-74. Review of Economic Dynamics 13, 824–842.CrossRefGoogle Scholar
Alpanda, S. and Ueberfeldt, A. (2016) Should Monetary Policy Lean Against Housing Market Booms? Bank of Canada Working Paper: No. 2016–19.Google Scholar
Arellano, C., Maliar, L., Maliar, S. and Tsyrennikov, V. (2016) Envelope condition method with an application to default risk models. Journal of Economic Dynamics & Control 69, 436–459.CrossRefGoogle Scholar
Bianchi, F. and Melosi, L. (2016) Modeling the Evolution of Expectations and Uncertainty in General Equilibrium. International Economic Review 57, 717–756.CrossRefGoogle Scholar
Boz, E., Daude, C. and Durdu, C. B. (2011) Emerging market business cycles: Learning about the trend. Journal of Monetary Economics 58, 616–631.CrossRefGoogle Scholar
Brinca, P., Chari, V. V., Kehoe, P. J. and McGrattan, E. (2016) Accounting for business cycles. In: Taylor, J. B. and Uhlig, H. (eds.), Handbook of Macroeconomics vol. 2, pp. 1013–1063. Amsterdam: Elsevier.Google Scholar
Bullard, J. (2016) The St. Louis Fed’s New Characterization of the Outlook for the U.S. Economy. Federal Reserve Bank of St. Louis, June 17, 2016.Google Scholar
Burns, A. F. and Mitchell, W. C. (1946) Measuring Business Cycles. New York: National Bureau of Economic Research.Google Scholar
Carroll, C. D. (2006) The method of endogenous grid points for solving dynamic stochastic optimal problems. Economics Letters 91, 312–320.CrossRefGoogle Scholar
Chari, V. V., Kehoe, P. and McGrattan, E. R. (2006) Business cycle accounting. Econometrica 75, 781–836.CrossRefGoogle Scholar
Chauvet, M. and Piger, J. M. (2003) Identifying business cycle turning points in real time. The Federal Reserve Bank of St. Louis Review March/April, 47–61.Google Scholar
Christiano, L. J., Eichenbaum, M. and Evans, C. L. (2005) Nominal rigidities and the dynamic effects of a shock to monetary policy. Journal of Political Economy 113, 1–45.CrossRefGoogle Scholar
Cooley, T. F. and Prescott, E. C. (1995) Economic growth and business cycles. In: Cooley, T. F. (ed.), Frontiers of Business Cycle Research, pp. 1–38. Princeton: Princeton University Press.CrossRefGoogle Scholar
Davig, T. (2004) Regime-switching debt and taxation. Journal of Monetary Economics 51, 837–859.CrossRefGoogle Scholar
Dufrénot, G. and Khayat, G. A. (2017) Monetary policy switching in the euro area and multiple steady states: An empirical investigation. Macroeconomic Dynamics 21, 1175–1188.CrossRefGoogle Scholar
Farmer, R. E. A., Waggoner, D. F. and Zha, T. (2011) Minimal state variable solutions to Markov switching rational expectations models. Journal of Economics Dynamics and Control 35, 2150–2166.CrossRefGoogle Scholar
Fernald, J. G. (2012) A Quarterly, Utilization-Adjusted Series on Total Factor Productivity. Federal Reserve Bank of San Francisco Working Paper 2012-19 (updated March 2014).CrossRefGoogle Scholar
Fernández-Villaverde, J., Guerrón-Quintana, P., Kuester, K. and Rubio-Ramírez, J. (2015) Fiscal volatility shocks and economic activity. American Economic Review 105, 3352–3384.CrossRefGoogle Scholar
Foerster, A., Rubio-Ramirez, J., Waggoner, D. F. and Zha, T. (2014) Perturbation Methods for Markov-Switching Models. National Bureau of Economic Research, Inc., NBER Working Paper: No. 20390.CrossRefGoogle Scholar
French, M. W. (2001) Estimating Changes in Trend Growth of Total Factor Productivity: Kalman and H-P Filters Versus a Markov-Switching Approach. Federal Reserve Board Finance and Economics Discussion Series No. 2001–44.Google Scholar
Gordon, R. J. (2012) Is U.S. Economic Growth Over? Faltering Innovation Confronts the Six Headwinds. National Bureau of Economic Research, Inc., NBER Working Paper: No. 18315.Google Scholar
Gordon, R. J. (2016) The Rise and Fall of American Growth: The U.S. Standard of Living since the Civil War. Princeton: Princeton University Press.CrossRefGoogle Scholar
Gourio, F. (2013) Credit risk and disaster risk. American Economic Journal: Macroeconomics 5, 1–34.Google Scholar
Greene, W. H. (2000). Econometric Analysis. New Jersey: Prentice Hall.Google Scholar
Hamilton, J. D. (1989) A new approach to the economic analysis of nonstationary time-series and the business cycle. Econometrica 57, 357–384.CrossRefGoogle Scholar
Justiniano, A. and Primiceri, G. E. (2006) The Time Varying Volatility of Macroeconomic Fluctuations. National Bureau of Economic Research, Inc., NBER Working Paper: No. 12022.CrossRefGoogle Scholar
Kahn, J. A. and Rich, R. W. (2007) Tracking the new economy: Using growth theory to detect changes in trend productivity. Journal of Monetary Economics 54, 1670–1701.CrossRefGoogle Scholar
Kim, C.-J. and Murray, C. J. (2002) Permanent and transitory components of recessions. Empirical Economics 27, 163–184.CrossRefGoogle Scholar
Kim, C.-J. and Piger, J. M. (2002) Common stochastic trends, common cycles, and asymmetry in economic fluctuations. Journal of Monetary Economics 49, 1189–1211.CrossRefGoogle Scholar
King, R. G., Plosser, C. I. and Rebelo, S. T. (1988) Production, growth and business cycles II: New directions. Journal of Monetary Economics 21, 309–341.CrossRefGoogle Scholar
King, R. G. and Rebelo, S. T. (1999) Resuscitating real business cycles. In Woodford, M. and Taylor, J. (eds.), Handbook of Macroeconomics vol. 1, pp. 927–1007. Amsterdam: Elsevier.CrossRefGoogle Scholar
Kuang, P. and Mitra, K. (2015) Long-Run Growth Uncertainty. University of Birmingham, Department of Economics Discussion Paper: No. 15-07.Google Scholar
Kydland, F. E. and Prescott, E. C. (1982) Time to build and Aggregate Fluctuations. Econometrica 50, 1345–1370.CrossRefGoogle Scholar
Lhuissier, S. (2018) The regime-switching volatility of euro area business cycles. Macroeconomic Dynamics 22, 426–469.CrossRefGoogle Scholar
Lorenzoni, G. (2009) A theory of demand shocks. American Economic Review 99, 2050–2084.CrossRefGoogle Scholar
Maih, J. (2015) Efficient Perturbation Methods for Solving Regime-Switching DSGE Models. Norges Bank Working Paper: No. 01-2015.CrossRefGoogle Scholar
Maliar, L. and Maliar, S. (2013) Envelope condition method versus endogenous grid method for solving dynamic programming problems. Economics Letters 120, 262–266.CrossRefGoogle Scholar
McConnell, M. M. and Perez-Quiros, G. (2000) Output fluctuations in the United States: What has changed since the early 1980’s?. American Economic Review 90, 1464–1476.CrossRefGoogle Scholar
Miranda, M. J. and Fackler, P. L. (2002) Applied Computational Economics and Finance. Cambridge, MA: MIT Press.Google Scholar
Nelson, C. R. and Plosser, C. R. (1982) Trends and random walks in macroeconomic time series: Some evidence and implications. Journal of Monetary Economics 10, 139–162.CrossRefGoogle Scholar
Pakko, M. R. (2002) What happens when the technology growth trend changes? Transition dynamics, capital growth, and the “new economy”. Review of Economic Dynamics 5, 376–407.CrossRefGoogle Scholar
Prescott, E. C. (1986) Theory ahead of business cycle measurement. Federal Reserve Bank of Minneapolis Quarterly Review 10, 9–22.Google Scholar
Richter, A. and Throckmorton, N. A. (2015) The consequences of an unknown debt target. European Economic Review 78, 76–96.CrossRefGoogle Scholar
Romer, P. M. (1990) Endogenous technological change. Journal of Political Economy 98, S71–S102.CrossRefGoogle Scholar
Smets, F. and Wouters, R. (2007) Shocks and frictions in US business cycles: A Bayesian DSGE approach. American Economic Review 97, 586–606.CrossRefGoogle Scholar
Stokey, N. L. and Lucas, R. E. Jr., with Prescott, E. C. (1989). Recursive Methods in Economic Dynamics. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Tauchen, G. (1986) Finite state Markov-chain approximations to univariate and vector autoregressions. Economics Letters 20, 177–181.CrossRefGoogle Scholar
Tortorice, D. L. (2016) The business cycles implications of fluctuating long run expectations. Brandeis University Working Paper: No. 100.CrossRefGoogle Scholar
Tortorice, D. L. (2018) Equity return predictability, time varying volatility and learning about the permanence of shocks. Journal of Economic Behavior and Organization 148, 315–343.CrossRefGoogle Scholar