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

• Published in: Macroeconomic dynamics Vol. 26; no. 2; pp. 394 - 418
• Main Authors: Chang-Jin, Kim, Kim, Jaeho
• Format: Journal Article
• Language: English
• Published: Cambridge Cambridge University Press 01.03.2022
• Subjects: Decomposition; Economic theory; GDP; Gross Domestic Product; Growth rate; Macroeconomics
• ISSN: 1365-1005; 1469-8056
• DOI: 10.1017/S1365100520000218

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.