Testing identification via heteroskedasticity in structural vector autoregressive models

被引：12

作者：

Luetkepohl, Helmut ^{[1
,2
]}

Meitz, Mika ^{[3
]}

Netsunajev, Aleksei ^{[4
]}

Saikkonen, Pentti ^{[5
]}

机构：

[1] Free Univ Berlin, DIW Berlin, Mohrenstr 58, D-10117 Berlin, Germany

[2] Free Univ Berlin, Sch Business & Econ, Mohrenstr 58, D-10117 Berlin, Germany

[3] Univ Helsinki, Discipline Econ, POB 17, FI-00014 Helsinki, Finland

[4] Tallinn Univ Technol, Sch Business & Governance, Dept Econ & Finance, Akad Tee 3, EE-12618 Tallinn, Estonia

[5] Univ Helsinki, Dept Math & Stat, POB 68, FI-00014 Helsinki, Finland

来源：

ECONOMETRICS JOURNAL | 2021年 / 24卷 / 01期

基金：

芬兰科学院;

关键词：

Heteroskedasticity; structural identification; vector autoregressive process;

D O I：

10.1093/ectj/utaa008

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Tests 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.

引用

页码：1 / 22

页数：22

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