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Methods to Estimate Dynamic Stochastic General Equilibrium Models

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  • Ruge-Murcia, Francisco J.

Abstract

This paper employs the one-sector Real Business Cycle model as a testing ground for four different procedures to estimate Dynamic Stochastic General Equilibrium (DSGE) models. The procedures are: 1) Maximum Likelihood (with and without measurement errors and incorporating priors), 2) Generalized Method of Moments, 3) Simulated Method of Moments, and 4) the Extended Method of Simulated Moments proposed by Smith (1993). Monte Carlo analysis shows that although all procedures deliver reasonably good estimates, there are substantial differences in statistical and computational efficiency in the small samples currently available to estimate DSGE models. The implications of the singularity of DSGE models for each estimation procedure are fully discussed.

Suggested Citation

  • Ruge-Murcia, Francisco J., 2002. "Methods to Estimate Dynamic Stochastic General Equilibrium Models," University of California at San Diego, Economics Working Paper Series qt4fc8x822, Department of Economics, UC San Diego.
  • Handle: RePEc:cdl:ucsdec:qt4fc8x822
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    More about this item

    Keywords

    DSGE models; estimation methods; Monte Carlo analysis; singularity;
    All these keywords.

    JEL classification:

    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: 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

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