GMM Estimation of Non-Gaussian Structural Vector Autoregression

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We consider estimation of the structural vector autoregression (SVAR) by the generalized method of moments (GMM). Given non-Gaussian errors and a suitable set of moment conditions, the GMM estimator is shown to achieve local identification of the structural shocks. The optimal set of moment conditions can be found by well-known moment selection criteria. Compared to recent alternatives, our approach has the advantage that the structural shocks need not be mutually independent, but only orthogonal, provided they satisfy a number of co-kurtosis conditions that prevail under independence. According to simulation results, the finite-sample performance of our estimation method is comparable, or even superior to that of the recently proposed pseudo maximum likelihood estimators. The two-step estimator is found to outperform the alternative GMM estimators. An empirical application to a small macroeconomic model estimated on postwar United States data illustrates the use of the methods.

Original languageEnglish
JournalJournal of Business and Economic Statistics
Number of pages13
Publication statusPublished - 2019
Externally publishedYes

    Research areas

  • GENERALIZED-METHOD, Generalized method of moments, INFERENCE, LIKELIHOOD, MOMENTS, MONETARY-POLICY, Non-Gaussian time series, SAMPLE PROPERTIES, SELECTION, Structural vector autoregression, VARS

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