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Bayesian estimation of DSGE models: identification using a diagnostic indicator

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  • Chadha, Jagjit S.
  • Shibayama, Katsuyuki

Abstract

Koop, Pesaran and Smith (2013) suggest a simple diagnostic indicator for the Bayesian estimation of the parameters of a DSGE model. They show that, if a parameter is well identiÖed, the precision of the posterior should improve as the (artiÖcial) data size T increases, and the indicator checks the speed at which precision improves. As it does not require any additional programming, a researcher just needs to generate artiÖcial data and estimate the model with increasing sample size, T. We apply this indicator to the benchmark Smets and Woutersí(2007) DSGE model of the US economy, and suggest how to implement this indicator on DSGE models

Suggested Citation

  • Chadha, Jagjit S. & Shibayama, Katsuyuki, 2018. "Bayesian estimation of DSGE models: identification using a diagnostic indicator," LSE Research Online Documents on Economics 90383, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:90383
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    1. Nikolay Iskrev, 2010. "Parameter identification in Dynamic Economic models," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    2. 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.
    3. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    4. Ratto, Marco & Roeger, Werner & Veld, Jan in 't, 2009. "QUEST III: An estimated open-economy DSGE model of the euro area with fiscal and monetary policy," Economic Modelling, Elsevier, vol. 26(1), pages 222-233, January.
    5. Iskrev, Nikolay, 2010. "Local identification in DSGE models," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 189-202, March.
    6. Canova, Fabio & Sala, Luca, 2009. "Back to square one: Identification issues in DSGE models," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 431-449, May.
    7. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
    8. Iskrev, Nikolay, 2008. "Evaluating the information matrix in linearized DSGE models," Economics Letters, Elsevier, vol. 99(3), pages 607-610, June.
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    Cited by:

    1. Pedro Chaim & Márcio Poletti Laurini, 2022. "Data Cloning Estimation and Identification of a Medium-Scale DSGE Model," Stats, MDPI, vol. 6(1), pages 1-13, December.
    2. Xu, Xin & Xu, Xiaoguang, 2023. "Monetary policy transmission modeling and policy responses," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    3. Ivashchenko, Sergey & Mutschler, Willi, 2020. "The effect of observables, functional specifications, model features and shocks on identification in linearized DSGE models," Economic Modelling, Elsevier, vol. 88(C), pages 280-292.

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    More about this item

    Keywords

    Bayesian estimation; dynamic stochastic general equilibrium models; identification.;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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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