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Multivariate detrending under common trend restrictions: Implications for business cycle research

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  • Kozicki, Sharon

Abstract

This paper outlines a methodology to detrend multiple time series under common trend restrictions. The same filters used to construct the estimated trend in univariate exercises are shown to be appropriate in multivariate studies with a single common trend. However, to estimate the common trend in the multivariate case, the filter is applied to a linear combination of series rather than to each series individually. An empirical example and simulation exercises illustrate the implications of common trend detrending for measurement of business cycle properties.
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  • Kozicki, Sharon, 1999. "Multivariate detrending under common trend restrictions: Implications for business cycle research," Journal of Economic Dynamics and Control, Elsevier, vol. 23(7), pages 997-1028, June.
  • Handle: RePEc:eee:dyncon:v:23:y:1999:i:7:p:997-1028
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    1. Terence C. Mills, 2013. "Trends, cycles and structural breaks," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 3, pages 45-60, Edward Elgar Publishing.
    2. Donald Robertson & Anthony Garratt & Stephen Wright, 2006. "Permanent vs transitory components and economic fundamentals," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 521-542.
    3. Athanasios Orphanides & Simon van Norden, 2002. "The Unreliability of Output-Gap Estimates in Real Time," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 569-583, November.
    4. James Morley & Benjamin Wong, 2020. "Estimating and accounting for the output gap with large Bayesian vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 1-18, January.
    5. Mario J. Crucini & Mototsugu Shintani, 2015. "Measuring international business cycles by saving for a rainy day," Canadian Journal of Economics, Canadian Economics Association, vol. 48(4), pages 1266-1290, November.
    6. Michael J. Dueker & Charles R. Nelson, 2003. "Business cycle detrending of macroeconomic data via a latent business cycle index," Working Papers 2002-025, Federal Reserve Bank of St. Louis.
    7. Julio J. Rotemberg, 1999. "A Heuristic Method for Extracting Smooth Trends from Economic Time Series," NBER Working Papers 7439, National Bureau of Economic Research, Inc.
    8. Panayotis G. Michaelides & Efthymios G. Tsionas & Angelos T. Vouldis & Konstantinos N. Konstantakis & Panagiotis Patrinos, 2018. "A Semi-Parametric Non-linear Neural Network Filter: Theory and Empirical Evidence," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 637-675, March.
    9. Tinsley, P A, 2002. "Rational Error Correction," Computational Economics, Springer;Society for Computational Economics, vol. 19(2), pages 197-225, April.
    10. Russell Barnett & Sharon Kozicki & Christopher Petrinec, 2009. "Parsing shocks: real-time revisions to gap and growth projections for Canada," Review, Federal Reserve Bank of St. Louis, vol. 91(Jul), pages 247-266.
    11. Fiona Atkins, 2005. "Financial Crises and Money Demand in Jamaica," Birkbeck Working Papers in Economics and Finance 0512, Birkbeck, Department of Economics, Mathematics & Statistics.
    12. Tahsin Saadi Sedik & Mr. Joannes Mongardini, 2003. "Estimating Indexes of Coincident and Leading Indicators: An Application to Jordan," IMF Working Papers 2003/170, International Monetary Fund.
    13. Amano, Robert & Coletti , Don & Murchison , Stephen, 2000. "Empirical Estimation and the Quarterly Projection Model: An Example Focusing on the External Sector," Working Paper Series 104, Sveriges Riksbank (Central Bank of Sweden).

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