GMM Estimation of Non-Gaussian Structural Vector Autoregression

Research output: Contribution to journalArticleScientificpeer-review

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, 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
ISSN0735-0015
DOIs
Publication statusPublished - 18 Jul 2019
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 511 Economics

Cite this

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title = "GMM Estimation of Non-Gaussian Structural Vector Autoregression",
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, 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.",
keywords = "511 Economics",
author = "Markku Lanne and Jani Luoto",
year = "2019",
month = "7",
day = "18",
doi = "10.1080/07350015.2019.1629940",
language = "English",
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publisher = "American Statistical Association",

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GMM Estimation of Non-Gaussian Structural Vector Autoregression. / Lanne, Markku; Luoto, Jani.

In: Journal of Business and Economic Statistics, 18.07.2019.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - GMM Estimation of Non-Gaussian Structural Vector Autoregression

AU - Lanne, Markku

AU - Luoto, Jani

PY - 2019/7/18

Y1 - 2019/7/18

N2 - 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.

AB - 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.

KW - 511 Economics

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JF - Journal of Business and Economic Statistics

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