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Estimating Nonlinear DSGE Models with Moments Based Methods

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  • Sergey Ivashchenko

Abstract

This article suggests new approach to approximation of moments of nonlinear DSGE models. These approximations are fast and accurate enough to use them for estimation of parameters of nonlinear DSGE models. A small financial DSGE model is repeatedly estimated by several approaches. Approximations of moments are close to moments calculated for large sample simulations. The quality of estimation with suggested approach is close to the Central Difference Kalman Filter (CDKF) based. At the same time suggested approach is much faster.

Suggested Citation

  • Sergey Ivashchenko, 2013. "Estimating Nonlinear DSGE Models with Moments Based Methods," EUSP Department of Economics Working Paper Series Ec-03/13, European University at St. Petersburg, Department of Economics.
  • Handle: RePEc:eus:wpaper:ec0313
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    Keywords

    DSGE; DSGE-VAR; GMM; nonlinear estimation;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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