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Bayesian estimation of the long-run trend of the US economy

Author

Listed:
  • Jaeho Kim

    (University of Oklahoma)

  • Sora Chon

    (Korea Development Institute)

Abstract

The main purpose of this paper is to scrutinize the long-run trend of the US real GDP during the post-war period. In the empirical analysis, we introduce multivariate unobserved components models that accommodate time-varying volatility bounded by an economically reasonable range. After accounting for the cointegration relationship among the real GDP, consumption, and investment, we find the following. (i) There was an abrupt and significant downturn in the long-run growth rate in 2007. (ii) The annualized long-run growth rate of the real GDP declined to approximately 1.69%, decreasing from a peak of nearly 3.21% during the 2000s. (iii) The bounded volatility assumption enables a trend-cycle decomposition of the US real GDP that matches the NBER’s recession dates.

Suggested Citation

  • Jaeho Kim & Sora Chon, 2022. "Bayesian estimation of the long-run trend of the US economy," Empirical Economics, Springer, vol. 62(2), pages 461-485, February.
    • Handle: RePEc:spr:empeco:v:62:y:2022:i:2:d:10.1007_s00181-021-02024-4
    DOI: 10.1007/s00181-021-02024-4
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    More about this item

    Keywords

    Structural break; Long-run trend; Unobserved components model; Bounded stochastic volatility;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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