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What Central Bankers Need to Know about Forecasting Oil Prices July 7 , 2013
| Content Provider | Semantic Scholar |
|---|---|
| Author | Baumeister, Christiane Kilian, Lutz |
| Copyright Year | 2013 |
| Abstract | Forecasts of the quarterly real price of oil are routinely used by international organizations and central banks worldwide in assessing the global and domestic economic outlook, yet little is known about how best to generate such forecasts. Our analysis breaks new ground in several dimensions. First, we address a number of econometric and data issues specific to real-time forecasts of quarterly oil prices. Second, we develop real-time forecasting models not only for U.S. benchmarks such as WTI crude oil, but we also develop forecasting models for the price of Brent crude oil, which has become increasingly accepted as the best measure of the global price of oil in recent years. Third, we design for the first time methods for forecasting the real price of oil in foreign consumption units rather than U.S. consumption units, taking the point of view of forecasters outside the U.S. In addition, we investigate the costs and benefits of allowing for time variation in vector autoregressive (VAR) model parameters and of constructing forecast combinations. We conclude that quarterly forecasts of the real price of oil from suitably designed VAR models estimated on monthly data generate the most accurate forecasts overall among a wide range of methods including forecasts based on oil futures prices, no-change forecasts and forecasts based on regression models estimated on quarterly data. JEL Code: Q43, C53, E32 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www-personal.umich.edu/~lkilian/bk070713r2.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Autoregressive model Bank (environment) Baseline (configuration management) Bayesian network Benchmark (computing) Consistency model Dimensions Euro currency Futures and promises Gibbs sampling Guanosine Diphosphate Inventory Mean squared prediction error Microsoft Outlook for Mac Mixed-data sampling Need to know Organisation for Economic Co-Operation and Development Personality inventories Petroleum Primality certificate Projections and Predictions Real-time clock Real-time data Real-time locating system Real-time transcription Ships Sixty Nine Specification Vector autoregression benefit standards characteristics |
| Content Type | Text |
| Resource Type | Article |
