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A regularization approach to the minimum distance estimation: application to structural macroeconomic estimation using IRFs

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  • Senay Sokullu

Abstract

This article considers the invertibility problem of the optimal weighting matrix encountered during Impulse Response Function Matching Estimation (IRFME) of Dynamic Stochastic General Equilibrium (DSGE) Models. We propose to use a regularized inverse and derive the asymptotic properties of the estimator. We show that the asymptotic distribution of our estimator converges to that of the optimal estimator which has important implications for testing the fit of the model. We demonstrate the small sample properties of the estimator by Monte Carlo simulation exercises. Finally, we use our estimator to estimate the model in Altig et al.

Suggested Citation

  • Senay Sokullu, 2020. "A regularization approach to the minimum distance estimation: application to structural macroeconomic estimation using IRFs," Oxford Economic Papers, Oxford University Press, vol. 72(2), pages 546-565.
    • Handle: RePEc:oup:oxecpp:v:72:y:2020:i:2:p:546-565.
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    File URL: http://hdl.handle.net/10.1093/oep/gpz045
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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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