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Evidence on Features of a DSGE Business Cycle Model from Bayesian Model Averaging

Author

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  • Rodney Strachan

    (Australian National University)

  • Herman K. van Dijk

    (Erasmus University Rotterdam, and VU University Amsterdam.)

Abstract

This discussion paper resulted in a publication in 'The International Economic Review' , 2013, 54(1), 385-402. The empirical support for features of a Dynamic Stochastic General Equilibrium model with two technology shocks is valuated using Bayesian model averaging over vector autoregressions. The model features include equilibria, restrictions on long-run responses, a structural break of unknown date and a range of lags and deterministicprocesses. We find support for a number of features implied by the economic model and the evidence suggests a break in the entire model structure around 1984 after which technology shocks appear to account for all stochastic trends. Business cycle volatility seems more due to investment specific technology shocks than neutraltechnology shocks.

Suggested Citation

  • Rodney Strachan & Herman K. van Dijk, 2012. "Evidence on Features of a DSGE Business Cycle Model from Bayesian Model Averaging," Tinbergen Institute Discussion Papers 12-025/4, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20120025
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    Cited by:

    1. Knotek, Edward S. & Zaman, Saeed, 2019. "Financial nowcasts and their usefulness in macroeconomic forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1708-1724.
    2. Rockey, James & Temple, Jonathan, 2016. "Growth econometrics for agnostics and true believers," European Economic Review, Elsevier, vol. 81(C), pages 86-102.
    3. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2012. "Combination schemes for turning point predictions," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 402-412.
    4. Knut Are Aastveit & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2017. "Have Standard VARS Remained Stable Since the Crisis?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(5), pages 931-951, August.
    5. Nalan Basturk & Cem Cakmakli & S. Pinar Ceyhan & Herman K. van Dijk, 2014. "On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14," Tinbergen Institute Discussion Papers 14-085/III, Tinbergen Institute, revised 04 Sep 2014.
    6. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    7. Nalan Basturk & Cem Cakmakli & S. Pinar Ceyhan & Herman K. van Dijk, 2013. "Historical Developments in Bayesian Econometrics after Cowles Foundation Monographs 10, 14," Tinbergen Institute Discussion Papers 13-191/III, Tinbergen Institute.
    8. Philip Liu & Konstantinos Theodoridis & Haroon Mumtaz & Francesco Zanetti, 2019. "Changing Macroeconomic Dynamics at the Zero Lower Bound," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 391-404, July.

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    More about this item

    Keywords

    Posterior probability; Dynamic stochastic general equilibrium model; Cointegration; Model averaging; Stochastic trend; Impulse response; Vector autoregressive model;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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