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WHATEVER HAPPENED TO THE BUSINESS CYCLE? A BAYESIAN ANALYSIS OF JOBLESS RECOVERIES

Published online by Cambridge University Press:  30 July 2010

Kristie M. Engemann  and
Michael T. Owyang
Kristie M. Engemann
Affiliation:
Federal Reserve Bank of St. Louis
Michael T. Owyang*
Affiliation:
Federal Reserve Bank of St. Louis
*
Address correspondence to: Michael T. Owyang, Research Division, Federal Reserve Bank of St. Louis, P.O. Box 442, St. Louis, MO 63166, USA; e-mail: owyang@stls.frb.org.
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Abstract

During the typical recovery from U.S. postwar period economic downturns, employment recovers to its prerecession level within months of the output trough. However, during the past two recoveries, employment has taken up to three years to achieve its prerecession benchmark. We propose a formal empirical model of business cycles with recovery periods to demonstrate that the past two recoveries have been statistically different from previous experiences. We find that this difference can be attributed to a shift in the speed of transition between business cycle regimes. Moreover, we find this shift results from both durable and nondurable manufacturing sectors losing their cyclical characteristics. We argue that this finding of acyclicality in post-1980 manufacturing sectors is consistent with previous hypotheses (e.g., improved inventory management) regarding the reduction in macroeconomic volatility over the same period. These results suggest a link between the two phenomena, which have heretofore been studied separately.

Keywords

Business CyclesGibbs SamplingSmooth Transition AutoregressionIndustrial Disaggregation
Type
Articles
Information
Macroeconomic Dynamics , Volume 14 , Issue 5 , November 2010 , pp. 709 - 726
Copyright
Copyright © Cambridge University Press 2010

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References

REFERENCES

Aaronson, Daniel, Rissman, Ellen R., and Sullivan, Daniel G. (2004a) Assessing the jobless recovery. Federal Reserve Bank of Chicago Economic Perspectives 28, 2–20.Google Scholar
Aaronson, Daniel, Rissman, Ellen R., and Sullivan, Daniel G. (2004b) Can sectoral reallocation explain the jobless recovery? Federal Reserve Bank of Chicago Economic Perspectives 28, 36–49.Google Scholar
Bachmann, Ruediger (2007) Understanding Jobless Recoveries—A Tale of Two Margins. Manuscript, Yale University.Google Scholar
Burns, Arthur F. and Mitchell, Wesley C. (1946) Measuring Business Cycles. New York: National Bureau of Economic Research.Google Scholar
Carlin, Bradley P., Gelfand, Alan E., and Smith, Adrian F. M. (1992) Hierarchical Bayesian analysis of changepoint problems. Applied Statistics 41, 389–405.CrossRefGoogle Scholar
Chib, Siddhartha (1995) Marginal likelihood from the Gibbs output. Journal of the American Statistical Association 90, 1313–1321.Google Scholar
Chib, Siddhartha and Greenberg, Edward (1995) Understanding the Metropolis–Hastings algorithm. American Statistician 49, 327–335.CrossRefGoogle Scholar
Chib, Siddhartha and Jeliazkov, Ivan (2001) Marginal likelihood from the Metropolis–Hastings output. Journal of the American Statistical Association 96, 270–281.Google Scholar
Dueker, Michael J. (2006) Using cyclical regimes of output growth to predict jobless recoveries. Federal Reserve Bank of St. Louis Review 88, 145–153.Google Scholar
Escribano, Alvaro and Jorda, Oscar (2001) Testing nonlinearity: Decision rules for selecting between logistic and exponential STAR models. Spanish Economic Review 3, 193–209.Google Scholar
Faberman, R. Jason (2004) Gross Job Flows over the Past Two Business Cycles: Not All “Recoveries” Are Created Equal. Working Paper 372, U.S. Bureau of Labor Statistics.Google Scholar
Faberman, R. Jason (2008) Job Flows, Jobless Recoveries, and the Great Moderation. Working Paper 08–11, Research Department, Federal Reserve Bank of Philadelphia.Google Scholar
Gelfand, Alan E. and Smith, Adrian F. M. (1990) Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85, 398–409.Google Scholar
Glosser, Stuart and Golden, Lonnie (2004) The changing nature of hours and employment adjustment in US manufacturing: A contributing cause of the jobless recovery? International Journal of Manpower 25, 618–642.Google Scholar
Groshen, Erica L. and Potter, Simon (2003) Has structural change contributed to a jobless recovery? Federal Reserve Bank of New York Current Issues in Economics and Finance 9, 1–7.Google Scholar
Hamilton, James D. (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57, 357–384.Google Scholar
Hetrick, Ron L. (2000) Analyzing the recent upward surge in overtime hours. Monthly Labor Review 123, 30–33.Google Scholar
Holmes, Mark J. and Silverstone, Brian (2006) Okun's law, asymmetries and jobless recoveries in the United States: A Markov-switching approach. Economics Letters 92, 293–299.CrossRefGoogle Scholar
Kim, Chang-Jin, Morley, James, and Piger, Jeremy (2005) Nonlinearity and the permanent effects of recessions. Journal of Applied Econometrics 20, 291–309.CrossRefGoogle Scholar
Koenders, Kathryn (2005) Long Expansions and Slow Recoveries: A Closer Look at Employment Fluctuations. Manuscript, Arizona State University.Google Scholar
Koenders, Kathryn and Rogerson, Richard (2005) Organizational dynamics over the business cycle: A view on jobless recoveries. Federal Reserve Bank of St. Louis Review 87, 555–579.Google Scholar
Lopes, Hedibert F. and Salazar, Esther (2006) Bayesian model uncertainty in smooth transition autoregressions. Journal of Time Series Analysis 27, 99–117.CrossRefGoogle Scholar
McConnell, Margaret M. and Perez-Quiros, Gabriel (2000) Output fluctuations in the United States: What has changed since the early 1980's? American Economic Review 90, 1464–1476.CrossRefGoogle Scholar
Morley, James and Piger, Jeremy (2006) The importance of nonlinearity in reproducing business cycle features. In Milas, Costas, Rothman, Philip, and van Dijk, Dick (eds.), Nonlinear Time Series Analysis of Business Cycles, pp. 75–95. Amsterdam: Elsevier Science.Google Scholar
Owyang, Michael T., Piger, Jeremy, and Wall, Howard J. (2008) A state-level analysis of the Great Moderation. Regional Science and Urban Economics 38, 578–589.CrossRefGoogle Scholar
Ramey, Valerie A. and Vine, Daniel J. (2006) Declining volatility in the U.S. automobile industry. American Economic Review 96, 1876–1889.Google Scholar
Schreft, Stacey L. and Singh, Aarti (2003) A closer look at jobless recoveries. Federal Reserve Bank of Kansas City Economic Review 88, 45–73.Google Scholar
Schreft, Stacey L., Singh, Aarti, and Hodgson, Ashley (2005) Jobless recoveries and the wait-and-see hypothesis. Federal Reserve Bank of Kansas City Economic Review 90, 81–99.Google Scholar
Schweitzer, Mark (2003) Another jobless recovery? Federal Reserve Bank of Cleveland Economic Commentary, 1–4.Google Scholar
Teräsvirta, Timo and Granger, Clive W. J. (1993) Modelling Nonlinear Dynamic Relationships. New York: Oxford University Press.Google Scholar
van Dijk, Dick, Teräsvirta, Timo, and Franses, Philip Hans (2002) Smooth transition autoregressive models—A survey of recent developments. Econometric Reviews 21, 1–47.CrossRefGoogle Scholar