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DSGE Estimation Using Generalized Empirical Likelihood and Generalized Minimum Contrast.

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This summary is machine-generated.

Empirical likelihood and Bayesian inference offer robust estimation for dynamic stochastic general equilibrium (DSGE) models, even with misspecification. Other methods show varied performance, with generalized method of moments and maximum likelihood performing poorly.

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dynamic stochastic general equilibriumempirical likelihoodmethod of momentsminimum Hellinger distanceminimum contrast

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Area of Science:

  • Econometrics
  • Computational Economics
  • Statistical Inference

Background:

  • Dynamic stochastic general equilibrium (DSGE) models are crucial for macroeconomic analysis.
  • Accurate estimation of DSGE models is essential for reliable policy insights.
  • Robustness of estimators under model misspecification is a significant concern.

Purpose of the Study:

  • To evaluate the performance of various estimators for DSGE models.
  • To assess the robustness of these estimators when model specifications are incorrect.
  • To compare generalized empirical likelihood and minimum contrast estimators.

Main Methods:

  • A Monte Carlo simulation experiment was conducted.
  • Performance was evaluated based on estimation accuracy and robustness.
  • Key estimators investigated include empirical likelihood, minimum contrast, Bayesian inference, generalized method of moments, and maximum likelihood.

Main Results:

  • Empirical likelihood (EL) and Bayesian inference demonstrated superior performance, even under misspecification.
  • Smoothed versions of estimators showed similar performance to non-smoothed counterparts.
  • Generalized method of moments (GMM) and maximum likelihood (ML) estimators performed less favorably compared to others.

Conclusions:

  • Empirical likelihood and Bayesian inference are recommended for robust DSGE model estimation.
  • Certain estimators, like exponentially tilted versions, are sensitive to atypical estimates.
  • The choice of estimator significantly impacts DSGE model performance and reliability.