GMM Estimation of Non-Gaussian Structural Vector Autoregression

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Title: GMM Estimation of Non-Gaussian Structural Vector Autoregression
Author: Lanne, Markku
Publisher: HECER, Helsinki Center of Economic Research
Date: 2018-01-18
Language: en
Belongs to series: HECER, Discussion Paper No. 423
ISSN: 1795-0562
Abstract: 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, containing a sufficient number of relevant co-kurtosis conditions, the GMM estimator is shown to achieve global identification of the parameters of the SVAR model up to changing the signs of the structural shocks. We also propose a procedure, based on well-known moment selection criteria, to find the optimal set of moment conditions among the sets that guarantee identification. 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 U.S. data illustrates the use of the methods. JEL Classification: C32 Keywords: structural VAR model, non-Gaussian time series, generalized method of moments

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