Structural Vector Autoregressions with Smooth Transition in Variances: The Interaction between U.S. Monetary Policy and the Stock Market

Abstract

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. Abstract. In structural vector autoregressive analysis identifying the shocks of interest via heteroskedasticity has become a standard tool. Unfortunately, the approaches currently used for modelling heteroskedasticity all have drawbacks. For instance, assuming known dates for variance changes is often unrealistic while more flexible models based on GARCH or Markov switching residuals are difficult to handle from a statistical and computational point of view. Therefore we propose a model based on a smooth change in variance that is flexible as well as relatively easy to estimate. The model is applied to a five-dimensional system of U.S. variables to explore the interaction between monetary policy and the stock market. It is found that previously used conventional identification schemes in this context are rejected by the data if heteroskedasticity is allowed for. Shocks identified via heteroskedasticity have a different economic interpretation than the shocks identified using conventional methods. Terms of use: Documents in

On the long-run neutrality of demand shocks

“…In order to shed more light on this issue, in this paper we relax the longrun neutrality assumption on demand shocks, and instead use information from changes in volatility to identify shocks following a recently developed method by Lütkepohl and Netšunajev (2014). 1 This method is advantageous to capture the volatility shifts in the data due to the Great Moderation.…”

Monetary Policy Shocks, Set-identifying Restrictions, and Asset Prices: A Benchmarking Approach for Analyzing Set-identified Models

“…More recently, Lanne, Meitz, and Saikkonen (2015) assume a non-Gaussian SVAR and confirm the findings of Bjørnland and Leitemo (2009) in rejecting the recursive identification scheme, and finding a significant instantaneous negative effect that, however, dies out quickly. In contrast, utilizing changes in the heteroskedasticity structure of the error term, Rigobon and Sack (2004) and Lütkepohl and Netšunajev (2014) find smaller, but relatively persistent negative effects. In a time-varying SVAR, Galí and Gambetti (2015) find negative short run effects that quickly turn into positive after impact especially in the 1980s and 1990s.…”

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

“…, K). This model was proposed and used by Lütkepohl and Netšunajev (2014b) in the context of SVAR analysis. For Gaussian u t , the log-likelihood can be written as in (5).…”

“…It is now a function of the transition parameters as well. An iterative procedure for estimation is discussed in detail by Lütkepohl and Netšunajev (2014b). Since the range of the smoothness and threshold parameters {γ, c} can be bounded, a grid search can be performed over the relevant range of these two parameters.…”

“…The volatility models that have been used in this context range from simple exogenous jumps in the residual variance (Rigobon (2003), Lanne and Lütkepohl (2008)) or a smooth transition between different volatility states (Lütkepohl and Netšunajev (2014b)) to more sophisticated GARCH processes (Normandin and Phaneuf (2004), Bouakez and Normandin (2010)) and Markov switching mechanisms (Lanne, Lütkepohl and Maciejowska (2010), Herwartz and Lütkepohl (2014)). 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)).…”