Hostname: page-component-7f64f4797f-p4db6 Total loading time: 0 Render date: 2025-11-04T02:29:41.711Z Has data issue: false hasContentIssue false
  • English
  • Français

TREND IN CYCLE OR CYCLE IN TREND? NEW STRUCTURAL IDENTIFICATIONS FOR UNOBSERVED-COMPONENTS MODELS OF U.S. REAL GDP

Published online by Cambridge University Press:  13 June 2014

Mardi Dungey ,
Jan P.A.M. Jacobs ,
Jing Tian  and
Simon van Norden
Mardi Dungey
Affiliation:
University of Tasmania, CFAP, University of Cambridge and CAMA
Jan P.A.M. Jacobs
Affiliation:
University of Groningen, University of Tasmania CAMA and CIRANO
Jing Tian
Affiliation:
University of Tasmania
Simon van Norden*
Affiliation:
HEC Montréal, CAMA, CIRANO and CIREQ
*
Address correpondence to: Simon van Norden, HEC Montréal, 3000 Chemin de la Cote Sainte Catherine, Montreal, QC H3T 2A7, Canada; e-mail: simon.van-norden@hec.ca.
Get access Rights & Permissions [Opens in a new window]

Abstract

A well-documented property of the Beveridge–Nelson trend–cycle decomposition is the perfect negative correlation between trend and cycle innovations. We show how this may be consistent with a structural model where permanent innovations enter the cycle or transitory innovations enter the trend, and that identification restrictions are necessary to make this structural distinction. A reduced-form unrestricted version is compatible with either option, but cannot distinguish which is relevant. We discuss economic interpretations and implications using U.S. real GDP data.

Keywords

Trend-Cycle DecompositionData RevisionState-Space Form

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.)

Article purchase

Temporarily unavailable

References

REFERENCES

Anderson, Heather, Low, Chian Nam, and Snyder, Ralph (2006) Single source of error state space approach to the Beveridge–Nelson decomposition. Economics Letters 91, 104–109.CrossRefGoogle Scholar
Baxter, Marianne and King, Robert G. (1999) Measuring business cycles: Approximate band-pass filters for economic time series. Review of Economics and Statistics 81, 575–593.CrossRefGoogle Scholar
Beveridge, Stephen and Nelson, Charles 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.CrossRefGoogle Scholar
Christiano, L. and Fitzgerald, T.J. (2003) The band-pass filter. International Economic Review 44, 435–465.CrossRefGoogle Scholar
Clark, Peter K. (1987) The cyclical component of U.S. economic activity. Quarterly Journal of Economics 102, 797–814.CrossRefGoogle Scholar
Harvey, Andrew C. (1989) Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge, UK: Cambridge University Press.Google Scholar
Harvey, Andrew C. (2006) Forecasting with unobserved time series models. In Elliott, Graham, Granger, Clive W.J., and Timmerman, Allan (eds.), Handbook of Economic Forecasting, Vol. 1, Chap. 7, pp. 327–412. Amsterdam: Elsevier.CrossRefGoogle Scholar
Harvey, Andrew C. and Koopman, Siem Jan (2000) Signal extraction and the formulation of unobserved components models. Econometrics Journal 3, 84–107.CrossRefGoogle Scholar
Jacobs, Jan P.A.M. (1998) Econometric Business Cycle Research. Boston: Kluwer Academic.CrossRefGoogle Scholar
Jacobs, Jan P.A.M. and Simon, van Norden (2011) Modeling data revisions: Measurement error and dynamics of “true” values. Journal of Econometrics 161, 101–109.Google Scholar
Jun, Duk Bin, Kim, Dong Soo, Park, Sungho, and Park, Myoung Hwan (2012) Parameter space restrictions in state space models. Journal of Forecasting 31, 109–123.CrossRefGoogle Scholar
Lee, Jaejoon and Nelson, Charles R. (2007) Expectation horizon and the Phillips curve: The solution to an empirical puzzle. Journal of Applied Econometrics 22, 161–178.CrossRefGoogle Scholar
Ma, Jun and Wohar, Mark E. (2013) An unobserved components model that yields business and medium-run cycles. Journal of Money, Credit and Banking 45, 1351–1373.CrossRefGoogle Scholar
Mills, Terence C. (2003) Modelling Trends and Cycles in Economic Time Series. Palgrave Texts in Econometrics. Houndmills, Basingstoke, Hampshire, UK: Palgrave Macmillan.CrossRefGoogle Scholar
Morley, James C. (2002) A state-space approach to calculating the Beveridge–Nelson decomposition. Economics Letters 75, 123–127.CrossRefGoogle Scholar
Morley, James C. (2011) The two interpretations of the Beveridge–Nelson decomposition. Macroeconomic Dynamics 15, 419–439.CrossRefGoogle Scholar
Morley, James C., Nelson, Charles R., and Zivot, Eric (2003) Why are the Beveridge–Nelson and unobserved-components decompositions of GDP so different? Review of Economics and Statistics 85, 235–243.CrossRefGoogle Scholar
Morley, James C., Panovska, Irina, and Sinclair, Tara M. (2013) Testing Stationarity for Unobserved Components Models. Working paper 2012 ECON 41A, University of New South Wales, Australian School of Business.CrossRefGoogle Scholar
Murray, Christian J. and Nelson, Charles R. (2004) The great depression and output persistence: A reply to Papell and Prodan. Journal of Money, Credit and Banking 36, 429–432.CrossRefGoogle Scholar
Nelson, Charles (2008) The Beveridge–Nelson decomposition in retrospect and prospect. Journal of Econometrics 146, 202–206.CrossRefGoogle Scholar
Oh, Kum Hwa, Zivot, Eric, and Creal, Drew (2008) The relationship between the Beveridge–Nelson decomposition and other permanent–transitory decompositions that are popular in economics. Journal of Econometrics 146, 207–219.CrossRefGoogle Scholar
Perron, Pierre and Wada, Tatsuma (2009) Let's take a break: Trends and cycle in US real GDP. Journal of Econometrics 56, 749–765.Google Scholar
Proietti, Tomasso (2006) Trend–cycle decompositions with correlated components. Econometric Reviews 25, 61–84.Google Scholar
Sargent, Thomas J. (1989) Two models of measurements and the investment accelerator. Journal of Political Economy 97, 251–287.CrossRefGoogle Scholar
Sinclair, Tara M. (2009) The relationships between permanent and transitory movements in U.S. output and the unemployment rate. Journal of Money, Credit and Banking 41, 529–542.CrossRefGoogle Scholar
Sinclair, Tara M. (2010) Asymmetry in the business cycle: Friedman's plucking model with correlated innovations. Studies in Nonlinear Dynamics and Econometrics 14, 235–243.Google Scholar
Watson, Mark W. (1986) Univariate detrending methods with stochastic trends. Journal of Monetary Economics 18, 49–75.CrossRefGoogle Scholar
Weber, Enzo (2011) Analyzing U.S. output and the great moderation by simultaneous unobserved components. Journal of Money, Credit and Banking 43, 1579–1597.CrossRefGoogle Scholar