Oil price shocks and economic growth: The volatility link

Abstract

This paper shows that oil shocks impact economic growth primarily through the conditional variance of growth. Our comparison of models focuses on density forecasts. Over a range of dynamic models, oil shock measures and data, we find a robust link between oil shocks and the volatility of economic growth. We then develop a new measure of oil shocks and show that it is superior to existing measures; it indicates that the conditional variance of growth increases in response to an indicator of the local maximum oil price exceedance. The empirical results uncover a large pronounced asymmetric response of the growth volatility to oil price changes. The uncertainty about future growth is considerably lower than with a benchmark AR(1) model when no oil shocks are present.

Introduction

This paper adds new results to the debate on how oil shocks impact real economic growth. Using a variety of oil shock measures, we find no evidence that oil shocks affect the conditional mean of economic growth. However, oil shocks have a strong robust impact on the conditional variance of growth. Related to initial studies (Hamilton, 1996, Mork, 1989) finding that oil price increases are relevant when they exceed the maximum oil price, we find that they lead to increases in the conditional variance of growth and provide the best density forecasts for future growth.

The importance of oil price movements and their impact on economic growth was raised by Hamilton (1983). However, the subsequent literature is unclear on the role, if any, that oil plays in the prediction of economic growth. The initial findings of Hamilton (1996) and Mork (1989) were that oil price increases are relevant when they exceed the maximum oil price, whereas oil price decreases have no significant effects on economic growth. These stylized facts were confirmed further by Hamilton, 2003, Hamilton, 2011 and Ravazzolo and Rothman (2013), among others.

This asymmetric response to oil shocks was challenged by Kilian and Vigfusson, 2011a, Kilian and Vigfusson, 2011b, who focussed on impulse response functions and found no significant difference between positive shocks and negative shocks. Hamilton (2011) argued that their results were caused by the use of different data sets, measures of oil prices and price adjustments.

The recent study by Kilian and Vigfusson (2013) provides a comprehensive predictive analysis of the effect of oil price shocks on economic growth. Among several economically-plausible nonlinear specifications, they find that including negative oil price shocks improves the economic growth forecasting further. In addition, the best predictive model preserves symmetry between positive and negative shocks.

One common feature across the majority of the literature is that the predictive models are nonlinear in oil prices, but linear in oil price shocks. First, a measure of oil price shocks is constructed, such as the net oil price increase (Hamilton, 1996) or large oil price change (Kilian & Vigfusson, 2013). Then, the constructed variable enters a linear model as one regressor so that its predictive performance can be examined in a homoskedastic setting. One exception is the study by Hamilton (2003), who modelled the nonparametric conditional mean function in order to study the nonlinear marginal effect of the oil price on economic growth.

Although our paper is the first to explore the impact of oil shocks on economic uncertainty, other papers by Elder and Serletis (2010) and Lee, Ni, and Ratti (1995) have investigated second-order moments from oil prices. Lee et al. (1995) argue that an oil shock standardized by a GARCH model is more important to the conditional mean of growth. This two-step estimation approach is extended to a bivariate GARCH-in-mean specification by Elder and Serletis (2010), who find that volatility in oil prices has a negative effect on several measures of output.

The most significant contribution of this paper is to demonstrate that oil shocks affect economic growth primarily through a volatility channel. We find little or no gain from including oil shocks in the conditional mean, but very significant forecast improvements when oil shocks enter the conditional variance of real growth. Of course, this volatility channel does not show up in the point forecasts of the conditional mean that the literature has focused on, but it becomes readily apparent in density forecasts.

Working with density forecasts has several advantages. First, a density forecast contains a complete description of future outcomes of growth, including the predictive mean. Second, a density forecast can provide a measure of economic uncertainty about the future, and will be sensitive to models with different conditional moment specifications. For example, volatility measures and density intervals from the predictive density will be sensitive to heteroskedasticity. Lastly, comparing models based on density forecasting leads to standard Bayesian methods of model comparison based on Bayes factors. Predictive or marginal likelihoods automatically penalize complex models that do not improve predictions. From a classical perspective, density forecasts can be evaluated through scoring rules (Elliott and Timmermann, 2008, Gneiting and Raftery, 2007), which have a close equivalence to Bayesian predictive likelihoods and Bayes factors.

This volatility channel is shown to be robust to different oil shock measures, real-time data and the use of the industrial production index for output. We consider five measures of oil price shocks, four from the academic literature and one developed in this paper. The analysis also considers a range of different lag structures for the dependent variable and the oil shock measures.

One oil shock measure, an indicator variable on the net oil price increase, results in the best density forecasts for economic growth. This specification dominates both a GARCH model and a hybrid GARCH model that includes these shocks and a stochastic volatility specification. This implies that economic uncertainty increases in response to a local maximum oil price exceedance. However, the impact of this exceedance is independent of the shock size, and we discuss some possible reasons for this. When an exceedance occurs, the standard deviation of real growth innovations, two quarters ahead, almost doubles. Thus, our empirical results uncover a large, pronounced asymmetric response of growth volatility to net oil price increases. One implication of our findings is that the uncertainty about future growth is considerably lower than that of a benchmark AR(1) model when no oil shocks are present.

The remainder of the paper is organized as follows. Section 2 reviews the data. Section 3 explains the out-of-sample density forecasts and the computation method. Section 4 defines our benchmark homoskedastic model along with other linear specifications. Section 5 then defines oil price shocks, while Section 6 introduces the model that allows oil shocks to affect the conditional mean and conditional variance of growth. The empirical results are discussed in Section 7, while Section 8 reports robustness checks. Section 9 concludes, and this is followed by an appendix that provides details on posterior simulation methods. Online supplementary material contains additional forecasting results.