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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Minimizing post-shock forecasting error through aggregation of outside information
Jilei Lin,Daniel J. Eck +1 more
TL;DR: A forecasting methodology for providing credible forecasts for time series that have recently undergone a shock is developed by borrowing knowledge from other timeseries that have undergone similar shocks for which post-shock outcomes are observed.
Oil Price Forecasts for the Long-Term:
TL;DR: The authors examined the accuracy of recursive oil price forecasts generated by the National Energy Modeling System model of the Energy Information Administration for forecast horizons of up to 15 years, and found that the EIA model is quite successful at beating the benchmark random walk model, but only at either end of the forecast horizon.
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.
Posted Content
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.