Structural vector autoregressions with heteroskedasticity: A review of different volatility models

Lütkepohl, Helmut; Netšunajev, Aleksei

doi:10.1016/j.ecosta.2016.05.001

Cited by 45 publications

(45 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth stressing that regardless of the type of identifying restrictions we impose on B, we do not have enough information in this stylized small-scale model to claim that e Yt is a demand or supply shock. Likewise, we do not have enough information to disentangle whether uncertainty shocks originate from economic policies and/or technology.5 We refer toLütkepohl (2013),Lütkepohl and Netšunajev (2017), andKilian and Lütkepohl (2017, chapter 14) for a review of this literature Chen and Netšunajev (2018). For this reason, and in line withLudvigson et al (2018a), we refer to e Yt as "real activity shock."…”

mentioning

confidence: 99%

Uncertainty across volatility regimes

Angelini

Bacchiocchi

Caggiano

et al. 2018

J of Applied Econometrics

View full text Add to dashboard Cite

Summary We propose a nonrecursive identification scheme for uncertainty shocks that exploits breaks in the volatility of macroeconomic variables and is novel in the literature on uncertainty. This approach allows us to simultaneously address two major questions in the empirical literature: Is uncertainty a cause or effect of decline in economic activity? Does the relationship between uncertainty and economic activity change across macroeconomic regimes? Results based on a small‐scale vector autoregression with US monthly data suggest that (i) uncertainty is an exogenous source of decline of economic activity, and (ii) the effects of uncertainty shocks amplify in periods of economic and financial turmoil.

show abstract

mentioning

confidence: 99%

Uncertainty across volatility regimes

Angelini

Bacchiocchi

Caggiano

et al. 2018

J of Applied Econometrics

View full text Add to dashboard Cite

show abstract

“…This outcome is interesting because, in a related study based on identification through heteroscedasticity, Lütkepohl and Netšunajev (2017a) found strong evidence against the restrictions for the US. Admittedly, this evidence is based on a quite different sample period.…”

Section: B ξBmentioning

confidence: 76%

“…The confidence intervals have a 68% level in a Gaussian environment. The standard errors are estimated with a fixed-design wild bootstrap conditioning on the transition probabilities, as proposed in Herwartz and Lütkepohl (2014) and also used in Lütkepohl and Netšunajev (2017a). The responses of the variables to both shocks are quite plausible.…”

Section: B ξBmentioning

confidence: 99%

The Relation between Monetary Policy and the Stock Market in Europe

Lütkepohl

Netšunajev

2018

Econometrics

Self Cite

View full text Add to dashboard Cite

Abstract:We use a cointegrated structural vector autoregressive model to investigate the relation between monetary policy in the euro area and the stock market. Since there may be an instantaneous causal relation, we consider long-run identifying restrictions for the structural shocks and also used (conditional) heteroscedasticity in the residuals for identification purposes. Heteroscedasticity is modelled by a Markov-switching mechanism. We find a plausible identification scheme for stock market and monetary policy shocks which is consistent with the second-order moment structure of the variables. The model indicates that contractionary monetary policy shocks lead to a long-lasting downturn of real stock prices.

show abstract

“…For example, the AIC criterion (Akaike (1974)), AIC = −2 log l + 2 × no of free parameters, the Hannan-Quinn criterion (Hannan and Quinn (1979)), HQ = −2 log l + 2 log log(T ) × no of free parameters, or the Bayesian criterion proposed by Schwarz (1978), BIC = −2 log l + log(T ) × no of free parameters, are standard criteria, some of which have been used also for choosing between volatility models (e.g., Lütkepohl and Netšunajev (2017)). However, so far little is known about the suitability for choosing between different volatility models for SVAR analysis.…”

Section: Model Selection Criteriamentioning

confidence: 99%

“…In some of the related literature even competing models are applied to the same data and it is unclear which of them best describes the time-varying volatility of 1 a given system of variables (see, e.g., Lütkepohl and Netšunajev (2017)). In such a situation having objective criteria that facilitate the selection of a model would be desirable.…”

Section: Introductionmentioning

confidence: 99%

Choosing between Different Time-Varying Volatility Models for Structural Vector Autoregressive Analysis

Luetkepohl

Schlaak

2017

SSRN Journal

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe 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.

show abstract

Structural vector autoregressions with heteroskedasticity: A review of different volatility models

Cited by 45 publications

References 34 publications

Uncertainty across volatility regimes

Uncertainty across volatility regimes

The Relation between Monetary Policy and the Stock Market in Europe

Choosing between Different Time-Varying Volatility Models for Structural Vector Autoregressive Analysis

Contact Info

Product

Resources

About