Structural Vector Autoregressions: Checking Identifying Long-Run Restrictions via Heteroskedasticity

Helmut Lütkepohl and Anton Velinov

No 1356, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

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 (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Pages: 28 p.
Date: 2014
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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https://www.diw.de/documents/publikationen/73/diw_01.c.435720.de/dp1356.pdf (application/pdf)

Related works:
Journal Article: STRUCTURAL VECTOR AUTOREGRESSIONS: CHECKING IDENTIFYING LONG-RUN RESTRICTIONS VIA HETEROSKEDASTICITY (2016)
Journal Article: Structural Vector Autoregressions: Checking Identifying Long-Run Restrictions via Heteroskedasticity (2016)
Working Paper: Structural Vector Autoregressions: Checking Identifying Long-run Restrictions via Heteroskedasticity (2014)
Working Paper: Structural vector autoregressions: Checking identifying long-run restrictions via heteroskedasticity (2014)
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