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On Identification of Bayesian DSGE Models

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  • Gary Koop
  • M. Hashem Pesaran
  • Ron P. Smith

Abstract

This article is concerned with local identification of individual parameters of dynamic stochastic general equilibrium (DSGE) models estimated by Bayesian methods. Identification is often judged by a comparison of the posterior distribution of a parameter with its prior. However, these can differ even when the parameter is not identified. Instead, we propose two Bayesian indicators of identification. The first follows a suggestion by Poirier of comparing the posterior density of the parameter of interest with the posterior expectation of its prior conditional on the remaining parameters. The second examines the rate at which the posterior precision of the parameter gets updated with the sample size, using data simulated at the parameter point of interest for an increasing sequence of sample sizes ( T ). For identified parameters, the posterior precision increases at rate T . For parameters that are either unidentified or are weakly identified, the posterior precision may get updated but its rate of update will be slower than T . We use empirical examples to demonstrate that these methods are useful in practice. This article has online supplementary material.

Suggested Citation

  • Gary Koop & M. Hashem Pesaran & Ron P. Smith, 2013. "On Identification of Bayesian DSGE Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 300-314, July.
  • Handle: RePEc:taf:jnlbes:v:31:y:2013:i:3:p:300-314 DOI: 10.1080/07350015.2013.773905
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    1. Stiassny, Alfred & Uhl, Christina, 2014. "Does Elderly Employment have an Impact on Youth Employment? A General Equilibrium Approach," Department of Economics Working Paper Series 4246, WU Vienna University of Economics and Business.
    2. Canova, Fabio & Ferroni, Filippo & Matthes, Christian, 2015. "Approximating Time Varying Structural Models With Time Invariant Structures," Working Paper 15-10, Federal Reserve Bank of Richmond.
    3. Kai Liu, 2014. "Public Finances, Business Cycles and Structural Fiscal Balances," Cambridge Working Papers in Economics 1411, Faculty of Economics, University of Cambridge.
    4. Yunus Aksoy & Henrique S. Basso & Tobias Grasl & Ron P. Smith, 2015. "Demographic Structure and Macroeconomic Trends," Birkbeck Working Papers in Economics and Finance 1501, Birkbeck, Department of Economics, Mathematics & Statistics.
    5. Alfred Stiassny & Christina Uhl, 2014. "Does Elderly Employment have an Impact on Youth Employment? A General Equilibrium Approach," Department of Economics Working Papers wuwp178, Vienna University of Economics and Business, Department of Economics.
    6. M. Hashem Pesaran & Ron P. Smith, 2014. "Tests of Policy Ineffectiveness in Macroeconometrics," CESifo Working Paper Series 4871, CESifo Group Munich.
    7. Pablo A. Guerrón-Quintana & James M. Nason, 2013. "Bayesian estimation of DSGE models," Chapters,in: Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 21, pages 486-512 Edward Elgar Publishing.
    8. Hashem M. Pesaran & Ron P. Smith, 2011. "Beyond the DSGE Straitjacket," CESifo Working Paper Series 3447, CESifo Group Munich.
    9. Zhijian Wang & Bin Xu, 2014. "Cycling in stochastic general equilibrium," Papers 1410.8432, arXiv.org.
    10. Kamber, Gunes & McDonald, Chris & Sander, Nick & Theodoridis, Konstantinos, 2016. "Modelling the business cycle of a small open economy: The Reserve Bank of New Zealand's DSGE model," Economic Modelling, Elsevier, vol. 59(C), pages 546-569.
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    12. Reicher, Christopher Phillip, 2013. "A note on the identification of dynamic economic models with generalized shock processes," Kiel Working Papers 1821, Kiel Institute for the World Economy (IfW).
    13. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, Elsevier.
    14. Chen, Xiaoshan & Kirsanova, Tatiana & Leith, Campbell, 2017. "How optimal is US monetary policy?," Journal of Monetary Economics, Elsevier, pages 96-111.
    15. Kőrösi, Gábor, 2016. "A lány továbbra is szolgál..
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      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 647-667.
    16. Enrique Martínez-García & Mark A. Wynne, 2014. "Assessing Bayesian Model Comparison in Small Samples," Advances in Econometrics,in: Bayesian Model Comparison, volume 34, pages 71-115 Emerald Publishing Ltd.
    17. Zhu, Yanyuan & Feng, Xiao, 2014. "China's national production function since 1997: A reinvestigation," Working Papers in Economics 2014,2, Bundeswehr University Munich, Economic Research Group.
    18. M Hashem Pesaran & Ron P Smith, 2017. "Tests of Policy Interventions in DSGE Models," BCAM Working Papers 1706, Birkbeck Centre for Applied Macroeconomics.
    19. Mutschler, Willi, 2015. "Identification of DSGE models—The effect of higher-order approximation and pruning," Journal of Economic Dynamics and Control, Elsevier, vol. 56(C), pages 34-54.
    20. Krogh, Tord S., 2015. "Macro frictions and theoretical identification of the New Keynesian Phillips curve," Journal of Macroeconomics, Elsevier, pages 191-204.
    21. Hsieh, Chih-Sheng & Lee, Lung fei, 2017. "Specification and Estimation of Network Formation and Network Interaction Models with the Exponential Probability Distribution," MPRA Paper 60726, University Library of Munich, Germany.
    22. Adjemian, Michael K. & Bruno, Valentina G. & Robe, Michel A., 2016. "Forward‐Looking USDA Price Forecasts," 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts 235931, Agricultural and Applied Economics Association.
    23. repec:eee:eecrev:v:100:y:2017:i:c:p:95-115 is not listed on IDEAS
    24. Elton Beqiraj & Massimiliano Tancioni, 2014. "Fiscal Consolidation and Sovereign Risk in the Euro-zone Periphery," Working Papers 167, University of Rome La Sapienza, Department of Public Economics.

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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