Open AccessPosted Content
Forecasting the Price of Oil
TLDR
In this article, the authors address some of the key questions that arise in forecasting the price of crude oil and evaluate the sensitivity of a baseline oil price forecast to alternative assumptions about future demand and supply conditions.Abstract:
We address some of the key questions that arise in forecasting the price of crude oil. What do applied forecasters need to know about the choice of sample period and about the tradeoffs between alternative oil price series and model specifications? Are real or nominal oil prices predictable based on macroeconomic aggregates? Does this predictability translate into gains in out-of-sample forecast accuracy compared with conventional no-change forecasts? How useful are oil futures markets in forecasting the price of oil? How useful are survey forecasts? How does one evaluate the sensitivity of a baseline oil price forecast to alternative assumptions about future demand and supply conditions? How does one quantify risks associated with oil price forecasts? Can joint forecasts of the price of oil and of U.S. real GDP growth be improved upon by allowing for asymmetries?read more
Citations
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The effect of oil supply shocks on US economic activity: What have we learned?
TL;DR: The authors show that most variation in the responses of real oil prices and US gross domestic product to oil supply disruptions is due to differences in identification assumptions and in the model specification, and that if conditions on a range of supply elasticity values supported by microeconomic estimates, the differences in the oil price responses diminishes.
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Forecasting oil prices: High-frequency financial data are indeed useful
Stavros Degiannakis,George Filis +1 more
TL;DR: In this paper, the importance of combining high frequency financial information along with the oil market fundamentals, in order to gain incremental forecasting accuracy for oil prices was examined, and the combination of the latter with high-frequency financial data significantly improved oil price forecasts, by reducing the RMSE of the no-change forecast by approximately 68%.
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Oil and US GDP: A Real-Time Out-of Sample Examination
TL;DR: The CAMAR Working Paper Series (ISSN 1892-2198) as discussed by the authors was published as CAMAR working papers series from 2010 to 2011, and from 2011 the series' name changed to CAMP working paper series.
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Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms
TL;DR: Numerical results demonstrated that the proposed combined model is not only robust but able to approximate the actual consumption satisfactorily, which is an effective tool in analysis for the energy market.
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Consumer Valuation of Fuel Costs and Tax Policy: Evidence from the European Car Market
TL;DR: The authors showed that despite a modest undervaluation of fuel costs, fuel taxes are more effective in reducing fuel usage than product taxes, and they also perform better in terms of welfare, even when usage demand is held fixed.
References
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Time series analysis
TL;DR: A ordered sequence of events or observations having a time component is called as a time series, and some good examples are daily opening and closing stock prices, daily humidity, temperature, pressure, annual gross domestic product of a country and so on.
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Coherent Measures of Risk
TL;DR: In this paper, the authors present and justify a set of four desirable properties for measures of risk, and call the measures satisfying these properties "coherent", and demonstrate the universality of scenario-based methods for providing coherent measures.
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Comparing Predictive Accuracy
TL;DR: The authors describes the advantages of these studies and suggests how they can be improved and also provides aids in judging the validity of inferences they draw, such as multiple treatment and comparison groups and multiple pre- or post-intervention observations.
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Impulse response analysis in nonlinear multivariate models
TL;DR: In this paper, the authors present a unified approach to impulse response analysis which can be used for both linear and nonlinear multivariate models and demonstrate the use of these measures for a nonlinear bivariate model of US output and the unemployment rate.
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The Economics of Exhaustible Resources
TL;DR: In this article, a discussion is confined in scope to absolutely irreplaceable assets, including peculiar problems of mineral wealth, free competition, maximum social value and state regulation, monopoly, value of a mine monopoly, retardation of production under monopoly, price effects from cumulated production, and the author's mathematically derived optimum solutions.