Avoiding Unintentionally Correlated Shocks in Proxy Vector Autoregressive Analysis

Noting that the shocks in vector autoregressive models can be correlated if a number of shocks is identified individually by multiple proxy variables, we propose a Generalized Method of Moments (GMM) approach for estimation that enforces uncorrelated shocks. We point out that if each proxy identifies exactly one shock and is uncorrelated with all other shocks, uncorrelatedness of the shocks provides over-identifying restrictions that can be used in our approach to improve the estimation efficiency of the structural parameters. It also opens up the possibility to use Hansen’s J-test to check the model specification. Our method generalizes other GMM proposals that work under more restrictive assumptions. We illustrate its usefulness by two empirical examples from the recent literature.

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Publication Details

Journal
Journal of Business and Economic Statistics
Published
2025-03-11
DOI
https://doi.org/10.1080/07350015.2025.2476699
Citations
1
Primary Topic
Monetary Policy and Economic Impact
Type
article
Field-Weighted Citation Impact
3.09

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article

Avoiding Unintentionally Correlated Shocks in Proxy Vector Autoregressive Analysis

Martin Bruns, Helmut Lütkepohl, James McNeil
1 citations
Journal of Business and Economic Statistics
Monetary Policy and Economic Impact
3.09
article

Avoiding Unintentionally Correlated Shocks in Proxy Vector Autoregressive Analysis

Martin Bruns, Helmut Lütkepohl, James McNeil
article en
1 citations

Abstract

Noting that the shocks in vector autoregressive models can be correlated if a number of shocks is identified individually by multiple proxy variables, we propose a Generalized Method of Moments (GMM) approach for estimation that enforces uncorrelated shocks. We point out that if each proxy identifies exactly one shock and is uncorrelated with all other shocks, uncorrelatedness of the shocks provides over-identifying restrictions that can be used in our approach to improve the estimation efficiency of the structural parameters. It also opens up the possibility to use Hansen’s J-test to check the model specification. Our method generalizes other GMM proposals that work under more restrictive assumptions. We illustrate its usefulness by two empirical examples from the recent literature.

Journal of Business and Economic StatisticsVol. 43(4)
University of East Anglia (GB), German Institute for Economic Research (DE), Dalhousie University (CA), Freie Universität Berlin (DE)
Social Sciences and Humanities Research Council of Canada
Reduced inequalities
Openalex Percentile: Top 13%
Monetary Policy and Economic Impact
3.09
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Avoiding Unintentionally Correlated Shocks in Proxy Vector Autoregressive Analysis — Martin Bruns, Helmut Lütkepohl, et al. · Journal of Business and Economic Statistics (2025) | TGRS Research Map | TGRS