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TREND-CYCLE DECOMPOSITIONS OF REAL GDP REVISITED: CLASSICAL AND BAYESIAN PERSPECTIVES ON AN UNSOLVED PUZZLE

Published online by Cambridge University Press:  18 June 2020

Chang-Jin Kim  and
Jaeho Kim
Chang-Jin Kim
Affiliation:
University of Washington
Jaeho Kim*
Affiliation:
University of Oklahoma
*
Address correspondence to: Jaeho Kim, Department of Economics, University of Oklahoma, Room 158, 308 Cate Center Drive Cate 1, Norman, OK 73072, USA. e-mail address: [email protected].
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Abstract

While Perron and Wada (2009) maximum likelihood estimation approach suggests that postwar US real GDP follows a trend stationary process (TSP), our Bayesian approach based on the same model and the same sample suggests that it follows a difference stationary process (DSP). We first show that the results based on the approach should be interpreted with caution, as they are relatively more subject to the ‘pile-up problem’ than those based on the Bayesian approach. We then directly estimate and compare the two competing TSP and DSP models of real GDP within the Bayesian framework. Our empirical results suggest that a DSP model is preferred to a TSP model both in terms of in-sample fits and out-of-sample forecasts.

Keywords

Pile-up ProblemProfile LikelihoodIntegrated LikelihoodTrend Stationary ProcessDifference Stationary ProcessOut-of-Sample Prediction
Type
Articles
Information
Macroeconomic Dynamics , Volume 26 , Issue 2 , March 2022 , pp. 394 - 418
Copyright
© Cambridge University Press 2020

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Footnotes

An earlier version of this article has been included as Chapter 1 of Jaeho Kim's Ph.D. dissertation at the University of Washington. Chang-Jin Kim acknowledges financial support from the Bryan C. Cressey Professorship at the University of Washington. Jaeho Kim acknowledges financial support from the Grover and Creta Ensley Fellowship in Economic Policy at the University of Washington. We thank Dukpa Kim, Charles R. Nelson, Richard Startz, Harald Uhlig, and Eric Zivot for helpful comments.

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