Choosing between different time-varying volatility models for structural vector autoregressive analysis

Helmut Lütkepohl and Thore Schlaak

The performance of information criteria and tests for residual heteroskedasticity for choosing between different models for time-varying volatility in the context of structural vector autoregressive analysis is investigated. Although it can be difficult to find the true volatility model with the selection criteria, using them is recommended because they can reduce the mean squared error of impulse response estimates substantially relative to a model that is chosen arbitrarily based on the personal preferences of a researcher. Heteroskedasticity tests are found to be useful tools for deciding whether time-varying volatility is present but do not discriminate well between different types of volatility changes. The selection methods are illustrated by specifying a model for the global market for crude oil.

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Year of publication:	July 4, 2017 ; This version: July 4, 2017
Authors:	Lütkepohl, Helmut ; Schlaak, Thore
Publisher:	Berlin : DIW Berlin, German Institute for Economic Research
Subject:	Structural vector autoregression \| identification via heteroskedasticity \| conditional heteroskedasticity \| smooth transition \| Markov switching \| GARCH \| VAR-Modell \| VAR model \| Volatilität \| Volatility \| ARCH-Modell \| ARCH model \| Heteroskedastizität \| Heteroscedasticity \| Schätzung \| Estimation \| Markov-Kette \| Markov chain \| Zeitreihenanalyse \| Time series analysis \| Schätztheorie \| Estimation theory