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- Wirtschaftswissenschaften (10) (remove)

- The propagation of regional shocks in housing markets: evidence from oil price shocks in Canada (2018)
- Shocks to the demand for housing that originate in one region may seem important only for that regional housing market. We provide evidence that such shocks can also affect housing markets in other regions. Our analysis focuses on the response of Canadian housing markets to oil price shocks. Oil price shocks constitute an important source of exogenous regional variation in income in Canada because oil production is highly geographically concentrated. We document that, at the national level, real oil price shocks account for 11% of the variability in real house price growth over time. At the regional level, we find that unexpected increases in the real price of oil raise housing demand and real house prices not only in oil-producing regions, but also in other regions. We develop a theoretical model of the propagation of real oil price shocks across regions that helps understand this finding. The model differentiates between oil-producing and non-oil-producing regions and incorporates multiple sectors, trade between provinces, government redistribution, and consumer spending on fuel. We empirically confirm the model prediction that oil price shocks are propagated to housing markets in non-oil-producing regions by the government redistribution of oil revenue and by increased interprovincial trade.

- The role of oil price shocks in causing U.S. recessions (2014)
- Although oil price shocks have long been viewed as one of the leading candidates for explaining U.S. recessions, surprisingly little is known about the extent to which oil price shocks explain recessions. We provide the first formal analysis of this question with special attention to the possible role of net oil price increases in amplifying the transmission of oil price shocks. We quantify the conditional recessionary effect of oil price shocks in the net oil price increase model for all episodes of net oil price increases since the mid-1970s. Compared to the linear model, the cumulative effect of oil price shocks over course of the next two years is much larger in the net oil price increase model. For example, oil price shocks explain a 3% cumulative reduction in U.S. real GDP in the late 1970s and early 1980s and a 5% cumulative reduction during the financial crisis. An obvious concern is that some of these estimates are an artifact of net oil price increases being correlated with other variables that explain recessions. We show that the explanatory power of oil price shocks largely persists even after augmenting the nonlinear model with a measure of credit supply conditions, of the monetary policy stance and of consumer confidence. There is evidence, however, that the conditional fit of the net oil price increase model is worse on average than the fit of the corresponding linear model, suggesting much smaller cumulative effects of oil price shocks for these episodes of at most 1%.

- A general approach to recovering market expectations from futures prices with an application to crude oil (2014)
- Futures markets are a potentially valuable source of information about market expectations. Exploiting this information has proved difficult in practice, because the presence of a time-varying risk premium often renders the futures price a poor measure of the market expectation of the price of the underlying asset. Even though the expectation in principle may be recovered by adjusting the futures price by the estimated risk premium, a common problem in applied work is that there are as many measures of market expectations as there are estimates of the risk premium. We propose a general solution to this problem that allows us to uniquely pin down the best possible estimate of the market expectation for any set of risk premium estimates. We illustrate this approach by solving the long-standing problem of how to recover the market expectation of the price of crude oil. We provide a new measure of oil price expectations that is considerably more accurate than the alternatives and more economically plausible. We discuss implications of our analysis for the estimation of economic models of energy-intensive durables, for the debate on speculation in oil markets, and for oil price forecasting.

- Impulse response matching estimators for DSGE models (2014)
- One of the leading methods of estimating the structural parameters of DSGE models is the VAR-based impulse response matching estimator. The existing asympotic theory for this estimator does not cover situations in which the number of impulse response parameters exceeds the number of VAR model parameters. Situations in which this order condition is violated arise routinely in applied work. We establish the consistency of the impulse response matching estimator in this situation, we derive its asymptotic distribution, and we show how this distribution can be approximated by bootstrap methods. Our methods of inference remain asymptotically valid when the order condition is satisfied, regardless of whether the usual rank condition for the application of the delta method holds. Our analysis sheds new light on the choice of the weighting matrix and covers both weakly and strongly identified DSGE model parameters. We also show that under our assumptions special care is needed to ensure the asymptotic validity of Bayesian methods of inference. A simulation study suggests that the frequentist and Bayesian point and interval estimators we propose are reasonably accurate in finite samples. We also show that using these methods may affect the substantive conclusions in empirical work.

- The impact of the shale oil revolution on U.S. oil and gasoline prices (2014)
- This article examines how the shale oil revolution has shaped the evolution of U.S. crude oil and gasoline prices. It puts the evolution of shale oil production into historical perspective, highlights uncertainties about future shale oil production, and cautions against the view that the U.S. may become the next Saudi Arabia. It then reviews the role of the ban on U.S. crude oil exports, of capacity constraints in refining and transporting crude oil, of differences in the quality of conventional and unconventional crude oil, and of the recent regional fragmentation of the global market for crude oil for the determination of U.S. oil and gasoline prices. It discusses the reasons for the persistent wedge between U.S. crude oil prices and global crude oil prices in recent years and for the fact that domestic oil prices below global levels need not translate to lower U.S. gasoline prices. It explains why the shale oil revolution unlike the shale gas revolution is unlikely to stimulate a boom in oil-intensive manufacturing industries. It also explores the implications of shale oil production for the transmission of oil price shocks to the U.S. economy.

- Inside the crystal ball: new approaches to predicting the gasoline price at the pump (2015)
- Although there is much interest in the future retail price of gasoline among consumers, industry analysts, and policymakers, it is widely believed that changes in the price of gasoline are essentially unforecastable given publicly available information. We explore a range of new forecasting approaches for the retail price of gasoline and compare their accuracy with the no-change forecast. Our key finding is that substantial reductions in the mean-squared prediction error (MSPE) of gasoline price forecasts are feasible in real time at horizons up to two years, as are substantial increases in directional accuracy. The most accurate individual model is a VAR(1) model for real retail gasoline and Brent crude oil prices. Even greater reductions in MSPEs are possible by constructing a pooled forecast that assigns equal weight to five of the most successful forecasting models. Pooled forecasts have lower MSPE than the EIA gasoline price forecasts and the gasoline price expectations in the Michigan Survey of Consumers. We also show that as much as 39% of the decline in gas prices between June and December 2014 was predictable.

- Understanding the decline in the price of oil since june 2014 (2015)
- Some observers have conjectured that oil supply shocks in the United States and in other countries are behind the plunge in the price of oil since June 2014. Others have suggested that a major shock to oil price expectations occurred when in late November 2014 OPEC announced that it would maintain current production levels despite the steady increase in non-OPEC oil production. Both conjectures are perfectly reasonable ex ante, yet we provide quantitative evidence that neither explanation appears supported by the data. We show that more than half of the decline in the price of oil was predictable in real time as of June 2014 and therefore must have reflected the cumulative effects of earlier oil demand and supply shocks. Among the shocks that occurred after June 2014, the most influential shock resembles a negative shock to the demand for oil associated with a weakening economy in December 2014. In contrast, there is no evidence of any large positive oil supply shocks between June and December. We conclude that the difference in the evolution of the price of oil, which declined by 44% over this period, compared with other commodity prices, which on average only declined by about 5%-15%, reflects oil-market specific developments that took place prior to June 2014.

- Anticipation, tax avoidance, and the price elasticity of gasoline demand (2015)
- Traditional least squares estimates of the responsiveness of gasoline consumption to changes in gasoline prices are biased toward zero, given the endogeneity of gasoline prices. A seemingly natural solution to this problem is to instrument for gasoline prices using gasoline taxes, but this approach tends to yield implausibly large price elasticities. We demonstrate that anticipatory behavior provides an important explanation for this result. We provide evidence that gasoline buyers increase gasoline purchases before tax increases and delay gasoline purchases before tax decreases. This intertemporal substitution renders the tax instrument endogenous, invalidating conventional IV analysis. We show that including suitable leads and lags in the regression restores the validity of the IV estimator, resulting in much lower and more plausible elasticity estimates. Our analysis has implications more broadly for the IV analysis of markets in which buyers may store purchases for future consumption.

- Forty years of oil price fluctuations: why the price of oil may still surprise us (2016)
- It has been forty years since the oil crisis of 1973/74. This crisis has been one of the defining economic events of the 1970s and has shaped how many economists think about oil price shocks. In recent years, a large literature on the economic determinants of oil price fluctuations has emerged. Drawing on this literature, we first provide an overview of the causes of all major oil price fluctuations between 1973 and 2014. We then discuss why oil price fluctuations remain difficult to predict, despite economistsâ€™ improved understanding of oil markets. Unexpected oil price fluctuations are commonly referred to as oil price shocks. We document that, in practice, consumers, policymakers, financial market participants and economists may have different oil price expectations, and that, what may be surprising to some, need not be equally surprising to others.

- Did the renewable fuel standard shift market expectations of the price of ethanol? (2016)
- It is commonly believed that the response of the price of corn ethanol (and hence of the price of corn) to shifts in biofuel policies operates in part through market expectations and shifts in storage demand, yet to date it has proved difficult to measure these expectations and to empirically evaluate this view. We utilize a recently proposed methodology to estimate the marketâ€™s expectations of the prices of ethanol, unfinished motor gasoline and crude oil at horizons from three months to one year. We quantify the extent to which price changes were anticipated by the market, the extent to which they were unanticipated, and how the risk premium in these markets has evolved. We show that the Renewable Fuel Standard (RFS) is likely to have increased ethanol price expectations by as much $1.45 in the year before and in the year after the implementation of the RFS had started. Our analysis of the term structure of expectations provides support for the view that a shift in ethanol storage demand starting in 2005 caused an increase in the price of ethanol. There is no conclusive evidence that the tightening of the RFS in 2008 shifted market expectations, but our analysis suggests that policy uncertainty about how to deal with the blend wall raised the risk premium in the ethanol futures market in mid-2013 by as much as 50 cents at longer horizons. Finally, we present evidence against a tight link from ethanol price expectations to corn price expectations and hence to storage demand for corn in 2005-06.