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

Luetkepohl, Helmut; Netšunajev, Aleksei

doi:10.2139/ssrn.2021757

Cited by 32 publications

(42 citation statements)

References 33 publications

(1 reference statement)

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“…For instance, Lanne and Lütkepohl () test competing theoretical assumptions to identify monetary policy shocks against the data. Lütkepohl and Netsunajev () model changes in volatility by means of a Markov switching mechanism, and test sign restrictions in a model of the crude oil market.…”

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confidence: 99%

Simulation Evidence on Theory‐based and Statistical Identification under Volatility Breaks

Herwartz

Plödt

2015

Oxf Bull Econ Stat

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Beside a priori theoretical assumptions on instantaneous or long‐run effects of structural shocks, sign restrictions have become a prominent means for structural vector autoregressive (SVAR) analysis. Moreover, changes in second order moments of systems of time series can be fruitfully exploited for identification purposes in SVARs. By means of Monte Carlo studies, we examine to what degree theory‐based and statistical identification approaches offer an accurate quantification of the true structural relations in a standard model for monetary policy analysis. Subsequently, we discuss how identifying information from theory‐based and statistical approaches can be combined on the basis of a low‐dimensional empirical model of US monetary policy.

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confidence: 99%

Simulation Evidence on Theory‐based and Statistical Identification under Volatility Breaks

Herwartz

Plödt

2015

Oxf Bull Econ Stat

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“…The latter is in line with standard economic intuition, but not with the results in Kilian (), where its effect on real activity was found positive. These data were recently also analysed by Lütkepohl and Netšunajev (), who informally labelled the three shocks by making use of sign restrictions in the Markov‐switching VAR model of Lanne, Lütkepohl and Maciejowska (). However, while their results did not clearly object the sign restrictions, the shocks did not seem very strongly identified.…”

Section: Empirical Illustrationsmentioning

confidence: 99%

“…To that end, sign constraints have been used in the previous statistical identification literature (see, e.g. Herwartz and Lütkepohl, ; Lütkepohl and Netšunajev, ; Lanne et al ., ). The idea of this approach is to visually check whether the impulse responses implied by the uniquely identified SVAR model satisfy the constraints.…”

Section: Introductionmentioning

confidence: 99%

Identification of Economic Shocks by Inequality Constraints in Bayesian Structural Vector Autoregression

Lanne

Luoto

2019

Oxf Bull Econ Stat

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Theories often make predictions about the signs of the effects of economic shocks on observable variables, thus implying inequality constraints on the parameters of a structural vector autoregression (SVAR). We introduce a new Bayesian procedure to evaluate the probabilities of such constraints, and, hence, to validate the theoretically implied economic shocks. We first estimate a SVAR, where the shocks are identified by statistical properties of the data, and subsequently label these statistically identified shocks by the Bayes factors calculated from their probabilities of satisfying given inequality constraints. In contrast to the related sign restriction approach that also makes use of theoretically implied inequality constraints, no restrictions are imposed. Hence, it is possible that only a subset or none of the theoretically implied shocks can be labelled. In the latter case, we conclude that the data do not lend support to the theory implying the signs of the effects in question. We illustrate the method by empirical applications to the crude oil market, and U.S. monetary policy.

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“…They argue that their findings are consistent with hysteresis models of unemployment. 8 See Psaradakis and Spagnolo (2003), Psaradakis and Spagnolo (2006), Herwartz and Lütkepohl (2011) and Lütkepohl and Netšunajev (2014) for the selection of the number of regimes. The tests for the number of regimes for each of the variables are not reported for the sake of brevity.…”

Section: Unit Root Testsmentioning

confidence: 99%

“…In order to determine the optimal number of regimes, we use the AIC, HQC and SC as discussed in Psaradakis and Spagnolo (2003), Psaradakis and Spagnolo (2006), Herwartz and Lütkepohl (2011) and Lütkepohl and Netšunajev (2014). The results are reported in Table 6 of Appendix.…”

Section: The Ms-svar Modelmentioning

confidence: 99%

A Markov-switching structural vector autoregressive model of boom and bust in the Australian labour market

Gaston

Rajaguru

2015

Empir Econ

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Major economic events, such as the global financial crisis, are episodes of identifiable duration that differ from other time periods. Using monthly data on the unemployment rate, labour force participation rate and employment for Australia for the period from 1978 to 2012, we estimate a Markov-switching SVAR model to examine the relationship between unemployment and labour force participation and the performance of the Australian labour market. Three distinct labour market regimes are identified. We find that the labour market switches between periods of low unemployment and high participation, prolonged periods of relative stability and short, sharp periods of high unemployment and low participation. A key finding is that, due to the behaviour of workers not in the labour force, the long-term effect of an upswing in labour hiring results in a lower unemployment rate and a lower labour force participation rate.Keywords Unemployment · Labour force participation · Regime switching JEL Classification O11 · O57 · C23 Noel Gaston and Gulasekaran Rajaguru: The comments of Felix Chan, Phillip Chindamo and two anonymous referees are gratefully acknowledged. The authors are also grateful to Lance Fisher and Jan-Egbert Sturm for feedback on an earlier version of the paper. As customary, the authors bear the responsibility for all errors and omissions.

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

Cited by 32 publications

References 33 publications

Simulation Evidence on Theory‐based and Statistical Identification under Volatility Breaks

Simulation Evidence on Theory‐based and Statistical Identification under Volatility Breaks

Identification of Economic Shocks by Inequality Constraints in Bayesian Structural Vector Autoregression

A Markov-switching structural vector autoregressive model of boom and bust in the Australian labour market

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