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Estimating Dynamic Macroeconomic Models : How Informative Are the Data?

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Abstract

Central banks have long used dynamic stochastic general equilibrium (DSGE) models, which are typically estimated using Bayesian techniques, to inform key policy decisions. This paper offers an empirical strategy that quantifies the information content of the data relative to that of the prior distribution. Using an off-the-shelf DSGE model applied to quarterly Euro Area data from 1970:3 to 2009:4, we show how Monte Carlo simulations can reveal parameters for which the model's structure obscures identification. By integrating out components of the likelihood function and conducting a Bayesian sensitivity analysis, we uncover parameters that are weakly informed by the data. The weak identification of some key structural parameters in our comparatively simple model should raise a red flag to researchers trying to draw valid inferences from, and to base policy upon, complex large-scale models featuring many parameters.

Suggested Citation

  • Daniel O. Beltran & David Draper, 2016. "Estimating Dynamic Macroeconomic Models : How Informative Are the Data?," International Finance Discussion Papers 1175, Board of Governors of the Federal Reserve System (U.S.).
  • Handle: RePEc:fip:fedgif:1175
    DOI: 10.17016/IFDP.2016.1175
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    Cited by:

    1. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    2. Broadbent, Ben & Di Pace, Federico & Drechsel, Thomas & Harrison, Richard & Tenreyro, Silvana, 2019. "The Brexit vote, productivity growth and macroeconomic adjustments in the United Kingdom," Discussion Papers 51, Monetary Policy Committee Unit, Bank of England.
    3. Mutschler, Willi, 2014. "Identification of DSGE Models - A Comparison of Methods and the Effect of Second Order Approximation," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100598, Verein für Socialpolitik / German Economic Association.

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    More about this item

    Keywords

    Bayesian estimation; Econometric modeling; Kalman filter; Likelihood; Local identifcation; Euro Area; MCMC; Policy-relevant parameters; Prior-versus-posterior comparison; Sensitivity analysis;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • F41 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Open Economy Macroeconomics

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