In this paper, we revisit the debate on the relationship between oil price shocks and stock market returns by replicating the quantile-on-quantile (QQ) regression model for the US stock market in Sim and Zhou (2015, Journal of Banking and Finance), and extending it to 15 countries. The classification of these countries as oil importers or oil exporters depends on their net position in crude oil trade. Our results indicate that the main finding by Sim and Zhou (2015) that large negative oil price shocks can bolster stock returns when markets are performing well is only partially supported by the three largest oil importers in our sample-China, Japan and India-during the period 1988:1-2007:12. However, when extending the study to more recent data (period 1988:1-2016:12), we find that China and India experience higher returns when markets perform well and there is a large positive oil price shock. Also, large positive oil price shocks often lead to higher stock market returns when markets perform well for both oil exporting countries-Canada, Russia, Norway-and moderately oil dependent countries-such as Malaysia, Philippines and Thailand. In most cases large negative oil price shocks depress further already poorly performing markets, as in Sim and Zhou (2015). These findings highlight that the relationship between the distributions of oil price shocks and stock market returns is not stable over time in most countries studied.Furthermore, the asymmetric effect between positive and negative oil price shocks observed in the US market by Sim and Zhou (2015) is less evident in most countries for both the baseline and extended periods.
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 This paper estimates a New Keynesian model of the U.S. economy over the period following the 2001 slump, a period for which the adequacy of monetary policy is intensely debated. To relate to this debate, we consider three alternative empirical in ‡ation series in the estimation. When using CPI or PCE, we …nd some support for the view that the Federal Reserve's policy was extra easy and may have led to equilibrium indeterminacy. Instead, when measuring in ‡ation with core PCE, monetary policy appears to have been reasonable and su¢ ciently active to rule out indeterminacy. We then relax the assumption that in ‡ation in the model is measured by a single indicator. We re-formulate the arti…cial economy as a factor model where the theory's concept of in ‡ation is the common factor to the three empirical in ‡ation series. We …nd that CPI and PCE provide better indicators of the latent concept while core PCE is less informative. Again, this procedure cannot dismiss indeterminacy. Terms of use: Documents in EconStor mayJEL codes E32, E52, E58.
Summary We provide a generalization of the Anderson–Rubin (AR) procedure for inference on parameters that represent the dependence between possibly endogenous explanatory variables and disturbances in a linear structural equation (endogeneity parameters). We stress the distinction between regression and covariance endogeneity parameters. Such parameters have intrinsic interest (because they measure the effect of latent variables, which induce simultaneity) and play a central role in selecting an estimation method (such as ordinary least‐squares or instrumental variable methods). We observe that endogeneity parameters might not be identifiable and we give the relevant identification conditions. These conditions entail a simple identification correspondence between regression endogeneity parameters and the usual structural parameters, while the identification of covariance endogeneity parameters typically fails as soon as global identification fails. We develop identification‐robust finite‐sample tests for joint hypotheses involving structural and regression endogeneity parameters, as well as marginal hypotheses on regression endogeneity parameters. For Gaussian errors, we provide tests and confidence sets based on standard Fisher critical values. For a wide class of parametric non‐Gaussian errors (possibly heavy‐tailed), we show that exact Monte Carlo procedures can be applied using the statistics considered. As a special case, this result also holds for usual AR‐type tests on structural coefficients. For covariance endogeneity parameters, we supply an asymptotic (identification‐robust) distributional theory. Tests for partial exogeneity hypotheses (for individual potentially endogenous explanatory variables) are covered as special cases. The proposed tests are applied to two empirical examples: the relation between trade and economic growth, and the widely studied problem of returns to education.
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