Testing identification via heteroskedasticity in structural vector autoregressive models

Helmut Lütkepohl, Mika Meitz, Aleksei Netšunajev and Pentti Saikkonen

The Econometrics Journal, 2021, vol. 24, issue 1, 1-22

Abstract: SummaryTests for identification through heteroskedasticity in structural vector autoregressive analysis are developed for models with two volatility states where the time point of volatility change is known. The tests are Wald-type tests for which only the unrestricted model, including the covariance matrices of the two volatility states, has to be estimated. The residuals of the model are assumed to be from the class of elliptical distributions, which includes Gaussian models. The asymptotic null distributions of the test statistics are derived, and simulations are used to explore their small-sample properties. Two empirical examples illustrate the usefulness of the tests in applied work.

Keywords: Heteroskedasticity; structural identification; vector autoregressive process (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (11)

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Related works:
Journal Article: Testing identification via heteroskedasticity in structural vector autoregressive models (2021)
Working Paper: Testing Identification via Heteroskedasticity in Structural Vector Autoregressive Models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:24:y:2021:i:1:p:1-22.

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