Disentangling Demand and Supply Shocks in the Crude Oil Market: How to Check Sign Restrictions in Structural Vars

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. Given the growing dissatisfaction with exclusion and long-run restrictions in structural vector autoregressive analysis, sign restrictions are becoming increasingly popular. So far there are no techniques for validating the shocks identified via such restrictions. Although in an ideal setting the sign restrictions specify shocks of interest, sign restrictions may be invalidated by measurement errors, data adjustments or omitted variables. We model changes in the volatility of the shocks via a Markov switching (MS) mechanism and use this devise to give the data a chance to object to sign restrictions. The approach is illustrated by considering a small model for the market of crude oil. Terms of use: Documents in

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

“…The model was first proposed for SVAR analysis by Lanne et al (2010). It has been used in a range of applied SVAR studies including Velinov and Chen (2015), Lütkepohl and Netšunajev (2014a), and Lütkepohl and Velinov (2016).…”

“…To illustrate the selection of a volatility model for a structural VAR analysis we use an example from Lütkepohl and Netšunajev (2014a) who consider a model for the global market for crude oil from Kilian (2009) with the following three variables: percent change in global crude oil production (∆prod t ), a log detrended index of real economic activity (q t ), and the log of the real price of oil (p t ). Thus, y t = (∆prod t , q t , p t ) .…”

Ambiguity and Time-Varying Risk Aversion in Sovereign Debt Markets

“…, that is, following Lanne et al (2010) and Lütkepohl and Netšunajev (2014) the distribution of the reduced form error term is assumed to depend on a discrete Markov process S t that can take on M values representing different regimes, S t ∈ {1, . .…”

What Drives Long-term Oil Market Volatility? Fundamentals versus Speculation

“…Since this contribution, an impressive list of empirical studies has investigated the effects of different types of oil shocks and supports Kilian's (2009) conclusion. See, among others, Lütkepohl and NetŠunajev (2014), Kilian and Murphy (2014), Baumeister and Peersman (2013), Kilian and Park (2009), Lombardi and Van Robays (2011) and Van Robays (2009, 2012). Because of the importance of global demand in driving oil volatility, Aastveit et al (2014) take a further step to explicitly analyze the contribution of the demand of different geographical regions.…”