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China Estimating Nonlinear DSGE Models with Moments Based Methods


  • Ivashchenko Sergey

    () (St. Petersburg Institute for Economics and Mathematics, Russian Academy of Sciences, St. Petersburg, 191187, Russia)


This article suggests a new approach to approximating moments for nonlinear DSGE models. This approach is fast and sufficiently accurate to estimate nonlinear DSGE models. A small financial DSGE model is repeatedly estimated by several modifications of the suggested approach. Approximations of the moments are close to the results of the large sample Monte Carlo estimation. The quality of parameter estimation using our suggested approach is close to the Central Difference Kalman Filter (CDKF); and our suggested approach is much faster.

Suggested Citation

  • Ivashchenko Sergey, 2015. "China Estimating Nonlinear DSGE Models with Moments Based Methods," Frontiers of Economics in China, Higher Education Press, vol. 10(1), pages 38-55, March.
  • Handle: RePEc:fec:journl:v:10:y:2015:i:1:p:38-55

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    DSGE; DSGE-VAR; GMM; nonlinear estimation;

    JEL classification:

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


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