Structural Vector Autoregressions: Checking Identifying Long-Run Restrictions via Heteroskedasticity
31 Pages Posted: 31 Jan 2014
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Structural Vector Autoregressions: Checking Identifying Long-Run Restrictions via Heteroskedasticity
Structural Vector Autoregressions: Checking Identifying Long-Run Restrictions via Heteroskedasticity
Date Written: January 2014
Abstract
Long-run restrictions have been used extensively for identifying structural shocks in vector autoregressive (VAR) analysis. Such restrictions are typically just-identifying but can be checked by utilizing changes in volatility. This paper reviews and contrasts the volatility models that have been used for this purpose. Three main approaches have been used, exogenously generated changes in the unconditional residual covariance matrix, changing volatility modelled by a Markov switching mechanism and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models. Using changes in volatility for checking long-run identifying restrictions in structural VAR analysis is illustrated by reconsidering models for identifying fundamental components of stock prices.
Keywords: Vector autoregression, heteroskedasticity, vector GARCH, conditional heteroskedasticity, Markov switching model
JEL Classification: C32
Suggested Citation: Suggested Citation