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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Journal ArticleDOI
The Role of Time-Varying Price Elasticities in Accounting for Volatility Changes in the Crude Oil Market
TL;DR: This article showed that both the short-run price elasticities of oil demand and of oil supply have declined considerably since the second half of the 1980s, which implies that small disturbances on either side of the oil market can generate large price responses without large quantity movements, which helps explain the latest run-up and subsequent collapse in the price of oil.
Book ChapterDOI
Forecasting the Price of Oil
TL;DR: 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 oil demand and oil supply conditions.
Journal ArticleDOI
Time-Varying Effects of Oil Supply Shocks on the US Economy
TL;DR: This paper investigated how the dynamic eects of oil supply shocks on the US economy have changed over time and found that a typical oil supply shock is currently characterized by a much smaller impact on world oil production and a greater eect on the real price of crude oil, but has a similar impact on US output and in-ability as in the 1970s.
Journal ArticleDOI
The role of speculation in oil markets: What have we learned so far
TL;DR: The authors found that the existing evidence is not supportive of an important role of speculation in driving the spot price of oil after 2003, and there is strong evidence that the co-movement between spot and futures prices reflects common economic fundamentals rather than the financialization of oil futures markets.
Journal ArticleDOI
Real-Time Forecasts of the Real Price of Oil
TL;DR: This paper showed that recursive vector autoregressive (VAR) models tend to have lower mean squared prediction error (MSPE) at short horizons than forecasts based on oil futures prices.
References
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Journal ArticleDOI
Quantifying the Risk of Deflation
Lutz Kilian,Simone Manganelli +1 more
TL;DR: In this paper, the authors propose formal and quantitative measures of the risk that future inflation will be excessively high or low relative to the range preferred by a private sector agent. And they illustrate their methodology by estimating the risks of deflation for the United States, Germany, and Japan for horizons of up to two years.
Journal ArticleDOI
Should oil prices receive so much attention? an evaluation of the predictive power of oil prices for the u.s. economy
TL;DR: The authors showed that the potential forecasting gains from including oil prices are in most cases close to 0, and that the only time oil shocks have an impact on gross domestic product (GDP) is when the price of oil is above the high of the previous year.
Journal ArticleDOI
Inference in Regression Models with Many Regressors
TL;DR: In this paper, the behavior of various standard and modified F, LR and LM tests in linear homoskedastic regressions is investigated, adapting an alternative asymptotic framework where the number of regressors and possibly restrictions grows proportionately to the sample size.
Posted Content
Bagging Time Series Models
Atsushi Inoue,Lutz Kilian +1 more
TL;DR: In this paper, a bootstrap aggregation of pre-test predictors (or bagging for short) is proposed as a means of constructing forecasts from multiple regression models with local-to-zero regression parameters and errors subject to possible serial correlation or conditional heteroskedasticity.