scholarly journals Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey

2014 ◽  
Author(s):  
Helmut Luetkepohl
Keyword(s):  
Vector Autoregressive ◽  
Structural Vector ◽  
Autoregressive Analysis

scholarly journals Testing identification via heteroskedasticity in structural vector autoregressive models

2020 ◽  
Author(s):  
Helmut Lütkepohl ◽  
Mika Meitz ◽  
Aleksei Netšunajev ◽  
Pentti Saikkonen
Keyword(s):  
Covariance Matrices ◽  
Small Sample ◽  
Test Statistics ◽  
Vector Autoregressive Models ◽  
Vector Autoregressive ◽  
Null Distributions ◽  
Small Sample Properties ◽  
Structural Vector ◽  
Autoregressive Analysis ◽  
Time Point

Summary 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.

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