GMM Estimation of Non-Gaussian Structural Vector Autoregression

Näytä kaikki kuvailutiedot

Julkaisun nimi: GMM Estimation of Non-Gaussian Structural Vector Autoregression
Tekijä: Lanne, Markku
Kuuluu julkaisusarjaan: HECER, Discussion Paper No. 423
ISSN: 1795-0562
Tiivistelmä: 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
Päiväys: 2018-01-18


Latausmäärä yhteensä: Ladataan...

Tiedosto(t) Koko Formaatti Näytä
HECER-DP423.pdf 665.6KB PDF Avaa tiedosto

Viite kuuluu kokoelmiin:

Näytä kaikki kuvailutiedot