Real-time forecast combinations for the oil price
Baumeister and Kilian (2015) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and other real‐time variables. Mindful of the importance of Brent crude oil as a global price benchmark, we extend consideration to the North Sea based measure and update In this paper, we forecast real prices of crude oil using real-time forecast combinations over time-varying parameter (TVP) models with single predictor. We reveal the significant predictability at all horizons up to 24 months. Forecasting the real price of oil - Time-variation and forecast combination The main focus of this work is to generate real-time forecasts of the real price of oil and to evaluate their out-of-sample performance. All the data used in this work are based on the premise of being subject to real-time data constraints. Forecast combinations have received little attention in the oil price forecasting literature to date. We demonstrate that over the last 20 years suitably constructed real-time forecast combinations would have been systematically more accurate than the no-change forecast at horizons up to 6 quarters or 18 months. Recently, a number of alternative econometric oil price forecasting models has been introduced in the literature and shown to be more accurate than the no-change forecast of the real price of oil. We investigate the merits of constructing real-time forecast combinations of six such models. Forecast combinations are promising for four reasons. Research paper: “Real-time Forecast Combinations for the Oil Price”, joint with Anthony Garratt and Yunyi Zhang. Journal of Applied Econometrics, 2019. Database, documentation, appendices and draft paper on this webpage.
2 May 2018 times and only the forecast combinations are able to constantly generate frequency financial data in forecasting the monthly real price of oil.
time. In section 6 we visually compare the accuracy of our pooled oil price forecasts to that of the EIA oil price forecasts during key episodes. In section 7 we illustrate how these forecasting tools may be used to produce real-time forecasts of the real and the nominal price of oil in a format consistent with that Baumeister and Kilian (2015) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and similar real-time variables. Baumeister and Kilian (2015) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and similar real-time variables. Baumeister and Kilian (2015) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and other real‐time variables. Mindful of the importance of Brent crude oil as a global price benchmark, we extend consideration to the North Sea based measure and update In this paper, we forecast real prices of crude oil using real-time forecast combinations over time-varying parameter (TVP) models with single predictor. We reveal the significant predictability at all horizons up to 24 months. Forecasting the real price of oil - Time-variation and forecast combination The main focus of this work is to generate real-time forecasts of the real price of oil and to evaluate their out-of-sample performance. All the data used in this work are based on the premise of being subject to real-time data constraints.
Real-time Forecast Combinations for the Oil Price – Discussion Paper no.494 National Institute of Economic and Social Research Real-time Forecast Combinations for the Oil Price Anthony Garratt, Shaun P. Vahey, Yunyi Zhang Abstract Baumeister and Kilian (2015) combine forecasts from six empirical models to predict real oil prices.
Downloadable! Baumeister and Kilian (2015) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their Authors: Anthony Garratt¹, Shaun P. Vahey², Yunyi Zhang³ Baumeister and Kilian (2015) combine forecasts from six empirical models to predict real oil prices.
Forecasts based on oil futures prices don't produce significant. MSPE reductions and have lower directional accuracy than. VAR models. ○ VAR forecasting
Real-time Forecast Combinations for the Oil Price – Discussion Paper no.494 National Institute of Economic and Social Research Real-time Forecast Combinations for the Oil Price Anthony Garratt, Shaun P. Vahey, Yunyi Zhang Abstract Baumeister and Kilian (2015) combine forecasts from six empirical models to predict real oil prices. Real-time forecast combinations for the oil price. The real-time data set is used in Garratt, A., Vahey, S. P. & Zhang, Y. (2018) `Real-time forecast combinations for the oil price'. We include the replication paper, database documentation and the data set. Please contact Yunyi Zhang (Yunyi.Zhang@warwick.ac.uk) if you have any questions.
The real-time data set is available for download from shaunvahey.com. Keywords : Real oil price forecasting, Brent crude oil, Forecast combina- tion. ∗. We thank
Baumeister and Kilian (2015) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and similar real-time variables. Baumeister and Kilian (2015) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and other real‐time variables. Mindful of the importance of Brent crude oil as a global price benchmark, we extend consideration to the North Sea based measure and update
View the crude oil price charts for live oil prices and read the latest forecast, news and technical analysis for Brent and WTI. Real-Time News Baumeister and Kilian (2015) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and other real‐time variables. Mindful of the importance of Brent crude oil as a global price benchmark, we extend consideration to the North Sea based measure and update time. In section 6 we visually compare the accuracy of our pooled oil price forecasts to that of the EIA oil price forecasts during key episodes. In section 7 we illustrate how these forecasting tools may be used to produce real-time forecasts of the real and the nominal price of oil in a format consistent with that Baumeister and Kilian (2015) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and similar real-time variables. Baumeister and Kilian (2015) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and similar real-time variables.