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GMM Estimation of Non-Gaussian Structural Vector Autoregression

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

In: Journal of Business and Economic Statistics, 2019.

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Lanne, Markku ; Luoto, Jani. / GMM Estimation of Non-Gaussian Structural Vector Autoregression. In: Journal of Business and Economic Statistics. 2019.

Bibtex

@article{29f687e75c414e919b8a52392d0efd15,
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 = "GENERALIZED-METHOD, Generalized method of moments, INFERENCE, LIKELIHOOD, MOMENTS, MONETARY-POLICY, Non-Gaussian time series, SAMPLE PROPERTIES, SELECTION, Structural vector autoregression, VARS",
author = "Markku Lanne and Jani Luoto",
year = "2019",
doi = "10.1080/07350015.2019.1629940",
language = "English",
journal = "Journal of Business and Economic Statistics",
issn = "0735-0015",
publisher = "Taylor & Francis Inc.",

}

RIS

TY - JOUR

T1 - GMM Estimation of Non-Gaussian Structural Vector Autoregression

AU - Lanne, Markku

AU - Luoto, Jani

PY - 2019

Y1 - 2019

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 - GENERALIZED-METHOD

KW - Generalized method of moments

KW - INFERENCE

KW - LIKELIHOOD

KW - MOMENTS

KW - MONETARY-POLICY

KW - Non-Gaussian time series

KW - SAMPLE PROPERTIES

KW - SELECTION

KW - Structural vector autoregression

KW - VARS

U2 - 10.1080/07350015.2019.1629940

DO - 10.1080/07350015.2019.1629940

M3 - Journal article

JO - Journal of Business and Economic Statistics

JF - Journal of Business and Economic Statistics

SN - 0735-0015

ER -