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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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Oil Price Shocks and Monetary Policy in a Data-Rich Environment
TL;DR: This paper examined the impact of different types of oil price shocks on the U.S. economy, using a factor-augmented VAR (FAVAR) approach, and found that demand shocks are more important than supply shocks in driving several macroeconomic variables.
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Predictive analytics of crude oil prices by utilizing the intelligent model search engine
TL;DR: An intelligent model search engine (IMSE), an integrated model selection algorithm, subject to the out of sample predictive performance and given set of explanatory variables for forecasting crude oil prices, and empirical results indicated that the proposed algorithm significantly outperformed a broad range of benchmark methodologies.
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Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example
TL;DR: In this paper, the authors compared various model combination schemes to find important predictors of the prices for the three selected fossil fuels, i.e., crude oil, natural gas and thermal coal.
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Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications
TL;DR: This article used a DSGE model to learn about the persistence of oil price movements and showed that this learning process alters the impact of oil shocks, making it time-dependent and consistent with the muted impact oil-price changes had on macroeconomic outcomes during the early 2000s and again over the past two years.
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Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework
TL;DR: In this article, the authors present results from modelling spot oil prices by Dynamic Model Averaging (DMA), based on a literature review and availability of data, the following oil price drivers have been selected: stock prices indices, stock prices volatility index, exchange rates, global economic activity, interest rates, supply and demand indicators and inventories level.
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.