Abstract
Extracting information from high-dimensional time series in the form of underlying factors is an increasingly popular methodology in forecasting applications. In this paper, principal component analysis (PCA) and three other methods for factor extraction are compared based on their deterministic and probabilistic forecasting performances using factor-augmented vector autoregressive (FAVAR) models. The existing PCA-based methods use only the contemporaneous covariance matrix of the data, while the other methods rely on weighted lagged cross-covariance matrices. Our empirical study considers four crude oil future price instruments and a 241 variable dataset of global energy prices and quantity, macroeconomic indicators, and financial series which are thought to influence oil price movements. Overall empirical findings are: (1) the PCA-based method performs better at shorter forecast horizons whereas the new methods involving lagged cross-covariance matrices tend to perform better at longer horizons (2 months or greater); (2) the performance ranking of the four methods under both deterministic and probabilistic forecasting is greatly affected by the number of factors included in the FAVAR models; (3) the forecast performances of the four methods are close to each other and no method performs uniformly better than the others. More research on the role of temporal dependence in determining the number of factors is warranted.
Similar content being viewed by others
Notes
The authors thank the referee for suggesting this extension and the use of Alessi et al. (2010) criterion for determining the number of lags.
Abbreviations
- AIC:
-
Akaike information criteria
- PCA:
-
Asymptotic principal components analysis
- BIC:
-
Bayesian information criteria
- CCM:
-
Cross-covariance matrices
- CDF:
-
Cumulative distribution function
- FAVAR:
-
Factor-augmented vector autoregressive
- GLY:
-
Generalized Lam and Yao
- LY:
-
Lam and Yao
- MLY:
-
Modified Lam and Yao
- PFS:
-
Prequential forecasting system
- PSM:
-
Multiple probability score
- RMSE:
-
Root-mean-squared error
- VAR:
-
Vector autoregressive
- MinVar(f):
-
The dispersion of probability forecasts, which cannot be explained by the conditional dispersion
- Scat(f):
-
The weighted average of the conditional variances
- Var(d):
-
Variance of the outcome index
- Bias2 :
-
The mis-calibration of the forecast
- Cov(f, d):
-
Covariance between the forecasts and the outcome index
References
Alessi L, Barigozzi M, Capasso M (2010) Improved penalization for determining the number of factors in approximate factor models. Stat Probab Lett 80(23–24):1806–1813
Alquist R, Kilian L (2010) What do we learn from the price of crude oil futures? J Appl Econ 25(4):539–573
Apergis N, Christou C, Payne J (2014) Precious metal markets, stock markets, and the macroeconomic environment: a FAVAR model approach. Appl Financ Econ 24(10):691–703
Bai J, Ng S (2002) Determining the number of factors in approximate factor models. Econometrica 70(1):191–221
Banerjii A, Marcellino M, Masten I (2014) Forecasting with factor-augmented error correction models. Int J Forecast 30(3):589–612
Barnett A, Mumtaz H, Theodoridis K (2014) Forecasting UK GDP growth and inflation under structural change: a comparison of models with time-varying parameters. Int J Forecast 30(1):129–143
Bernanke BS, Boivin J, Eliasz P (2005) Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach. Q J Econ 120(1):387–422
Bessler DA, Ruffley R (2004) Prequential analysis of stock market returns. Appl Econ 36(5):399–412
Boivin J, Ng S (2006) Are more data always better for factor analysis? J Econ 127:169–194
Brier GW (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 78(1):1–3
Casillas-Olvera G, Bessler DA (2006) Probability forecasting and central bank accountability. J Policy Model 28(2):223–234
Chevillon G, Rifflart C (2009) Physical market determinants of the price of crude oil and the market premium. Energy Econ 31(4):537–549
Cifarelli G, Paladino G (2010) Oil price dynamics and speculation: a multivariate financial approach. Energy Econ 32(2):363–372
D’Agostino A, Giannone D (2012) Comparing alternative predictors based on large-panel factor models. Oxf Bull Econ Stat 74(2):306–326
Das S, Gupta R, Kabundi A (2011) Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models. J Forecast 30(2):288–302
Datastream (2015) Thomson Reuters Datastream subscription service, Texas A&M University Library. Accessed 2 Mar 2015
Dawid AP (1984) Statistical theory: the prequential approach. J R Stat Soc 147(2):278–292
Diebold FX (2015) Comparing predictive accuracy, 20 years later: a personal perspective on the use and abuse of Diebold–Mariano tests. J Bus Econ Stat 33:1–9
Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 13:253–263
Duangnate K (2015) Essays on the dynamics of and forecasting ability within the U.S.A energy sector. Ph.D. Dissertation, Texas A&M University, College Station, TX
Eickmeier S, Ziegler C (2008) How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach. J Forecast 27(3):237–265
Erb CB, Harvey CR (2006) The strategic and tactical value of commodity futures. Financ Anal J 62(2):69–97
Favero CA, Niu L, Sala L (2012) Term structure forecasting: no-arbitrage restrictions versus large information set. J Forecast 31(2):124–156
Forni M, Hallin M, Lippi M, Reichlin L (2005) The generalized dynamic factor model: identification and estimation. Rev Econ Stat 82(4):540–554
Geweke J (1997) The dynamic factor analysis of economic time series. In: Aigner DJ, Goldberger AS (eds) Latent variables in socio-economic models. North-Holland, Amsterdam
Gneiting T (2008) Editorial: probability forecasting. J R Stat Soc Ser A (Statistics in Society) 171:319–321
Gneiting T, Katzfuss M (2014) Probabilistic forecasting. Ann Rev Stat Appl 1:125–151
Gupta R, Kabundi A (2010) The effect of monetary policy on house price inflation: a factor augmented vector autoregression (FAVAR) approach. J Econ Stud 37(6):616–626
Hong SW (2012) Three essays on price dynamics and causations among energy markets and macroeconomic information. Ph.D. Dissertation, Texas A&M University, College Station, TX
Ielpo F (2015) Forward rates, monetary policy and the economic cycle. J Forecast 34(4):241–260
Ipatova E (2014) Essays on factor models, application to the energy markets. Ph.D. Dissertation, City University London. London, England. http://openaccess.city.ac.uk/3666/1/Ipatova._Ekaterina.pdf. Accessed 15 Oct 2014
Kilian L (2008) Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market. CEPR Discussion paper no. 5994. University of Michigan. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=975262. Accessed 21 Apr 2016
Kling KL, Bessler DA (1989) Calibration-based predictive distributions: an application of prequential analysis to interest rates, money, prices, and output. J Bus 62(4):477–499
Koop G (2013) Forecasting with medium and large Bayesian VARs. J Appl Econ 28(2):177–203
Lam C, Yao Q (2012) Factor modeling for high-dimensional time series: inference for the number of factors. Ann Stat 40(2):694–726
Ledoit O, Wolf M (2003) Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J Empir Finance 10(5):603–621
Li J, Chen W (2014) Forecasting macroeconomic time series: LASSO-based approaches and their forecasting combinations with dynamic factor models. Int J Forecast 30(4):996–1015
Luciani M (2014) Forecasting with approximate dynamic factor models: the role of nonpervasive shocks. Int J Forecast 30(1):20–29
Matteson DS, Tsay RS (2011) Dynamic orthogonal components for multivariate time series. J Am Stat Assoc 106(496):1450–1463
Merino A, Ortiz A (2005) Explaining the so-called ‘price premium’ in oil markets. OPEC Rev 29(2):133–152
Moench E (2008) Forecasting the yield curve in a data-rich environment: a no-arbitrage factor-augmented VAR approach. J Econ 146(1):26–43
Morana C (2013) Oil price dynamics, macro-finance interactions and the role of financial speculation. J Bank Finance 37(1):206–226
Mumtaz H, Zabczyk P, Ellis C (2011) What lies beneath? A time-varying FAVAR model for the UK transmission mechanism. European Central Bank working paper series no. 1320. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.204.6895&rep=rep1&type=pdf. Accessed 21 Apr 2016
Murphy AH (1970) The ranked probability score and the probability score: a comparison. Mon Weather Rev 98(12):917–924
Nazlioglu S, Soytas U, Gupta R (2015) Oil prices and financial stress: a volatility spillover analysis. Energy Policy 82:278–288
Raknerud A, Skjerpen T, Swensen AR (2010) Forecasting key macroeconomic variables from a large number of predictors: a state space approach. J Forecast 29(4):367–387
Salisu A, Oloko T (2015) Modeling oil price-US stock nexus: a VARMA-BEKK-AGARCH approach. Energy Econ 50:1–12
Schumacher C (2007) Forecasting German GDP using alternative factor models based on large datasets. J Forecast 26(4):271–302
Stock JH, Watson MW (2002) Forecasting using principal components from a large number of predictors. J Am Stat Assoc 97(460):1167–1179
Stock JH, Watson MW (2005a) Implications of dynamic factor models for VAR analysis. Working paper 11467. National Bureau of Economic Research. Cambridge, MA. http://www.nber.org.lib-ezproxy.tamu.edu:2048/papers/w11467.pdf. Accessed 28 May 2015
Stock JH, Watson MW (2005b) An empirical comparison of methods for forecasting using many predictors. Manuscript, Princeton University. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.421.3470&rep=rep1&type=pdf. Accessed 2 Apr 2016
Tsay RS (2014) Multivariate time series analysis: with R and financial applications. Wiley, Hoboken
U.S. Energy Information Administration (2015) Monthly energy review. http://www.eia.gov/totalenergy/data/monthly/index.cfm?src=email#prices. Accessed 2 Mar 2015
Vargas-Silva C (2008) The effect of monetary policy on housing: a factor-augmented vector autoregression (FAVAR) approach. Appl Econ Lett 15(10):749–752
Yang CW, Hwang MJ, Huang NN (2002) An analysis of factors affecting price volatility of the US oil market. Energy Econ 24(2):107–119
Yates F (1988) Analyzing the accuracy of probability judgments for multiple events: an extension of the covariance decomposition. Organ Behav Hum Decis Process 41(3):281–299
Zagaglia P (2010) Macroeconomic factors and oil futures prices: a data-rich model. Energy Econ 32(2):409–417
Zhang YJ, Fan Y, Tsai HT, Wei YM (2008) Spillover effect of US dollar exchange rate on oil prices. J Policy Model 30(6):973–991
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
The views expressed in this paper are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of Chicago, the Federal Reserve System, or Texas A&M University.
Appendices
Appendix A: Data series used in the empirical analyses
Series description | Units | Source | Transformationa | Category |
---|---|---|---|---|
Cushing, OK WTI spot price | USD/barrel | DS | 3 | |
WTI 1-month-ahead futures | USD/barrel | DS | 3 | |
WTI 6-month-ahead futures | USD/barrel | DS | 3 | |
WTI 12-month-ahead futures | USD/barrel | DS | 3 | |
F.O.B. Cost of Crude Oil Imports From Mexico | USD/barrel | EIA | 3 | Import costs |
F.O.B. Cost of Crude Oil Imports From All OPEC Countries | USD/barrel | EIA | 3 | |
F.O.B. Cost of Crude Oil Imports From All Non-OPEC Countries | USD/barrel | EIA | 3 | |
Landed Cost of Crude Oil Imports From Angola | USD/barrel | EIA | 3 | |
Landed Cost of Crude Oil Imports From Canada | USD/barrel | EIA | 3 | |
Landed Cost of Crude Oil Imports From Mexico | USD/barrel | EIA | 3 | |
Landed Cost of Crude Oil Imports From Nigeria | USD/barrel | EIA | 3 | |
Landed Cost of Crude Oil Imports From Saudi Arabia | USD/barrel | EIA | 3 | |
Landed Cost of Crude Oil Imports From Venezuela | USD/barrel | EIA | 3 | |
Landed Cost of Crude Oil Imports From Persian Gulf Nations | USD/barrel | EIA | 3 | |
Landed Cost of Crude Oil Imports From All OPEC Countries | USD/barrel | EIA | 3 | |
Landed Cost of Crude Oil Imports From All Non-OPEC Countries | USD/barrel | EIA | 3 | |
Unleaded Regular Gasoline, US City Average Retail Price | USD/gal | EIA | 3 | Refined |
Unleaded Premium Gasoline, US City Average Retail Price | USD/gal | EIA | 3 | |
All Grades of Gasoline, US City Average Retail Price | USD/gal | EIA | 3 | |
Refiner Price of Finished Motor Gasoline to End Users | USD/gal | EIA | 3 | |
Refiner Price of Kerosene-Type Jet Fuel to End Users | USD/gal | EIA | 3 | |
Refiner Price of No. 2 Diesel Fuel to End Users | USD/gal | EIA | 3 | |
Refiner Price of Finished Motor Gasoline for Resale | USD/gal | EIA | 3 | |
Refiner Price of Kerosene-Type Jet Fuel for Resale | USD/gal | EIA | 3 | |
Refiner Price of No. 2 Fuel Oil for Resale | USD/gal | EIA | 3 | |
Refiner Price of No. 2 Diesel Fuel for Resale | USD/gal | EIA | 3 | |
Refiner Price of Residual Fuel Oil, Sulfur Content Less Than or Equal to 1 Percent, Sales for Resale | USD/gal | EIA | 3 | |
Refiner Price of Residual Fuel Oil, Sulfur Content Less Than or Equal to 1 Percent, Sales to End Users | USD/gal | EIA | 3 | |
Refiner Price of Residual Fuel Oil, Sulfur Content Greater Than 1 Percent, Sales to End Users | USD/gal | EIA | 3 | |
Refiner Price of Residual Fuel Oil, Average, Sales for Resale | USD/gal | EIA | 3 | |
Refiner Price of Residual Fuel Oil, Average, Sales to End Users | USD/gal | EIA | 3 | |
Coal Consumed by the Commercial Sector | Trillion Btu | EIA | 3 | Consumption |
Natural Gas Consumed by the Commercial Sector (Excluding Supplemental Gaseous Fuels) | Trillion Btu | EIA | 4 | |
Petroleum Consumed by the Commercial Sector (Excluding Biofuels) | Trillion Btu | EIA | 3 | |
Total Fossil Fuels Consumed by the Commercial Sector | Trillion Btu | EIA | 4 | |
Conventional Hydroelectric Power Consumed by the Commercial Sector | Trillion Btu | EIA | 2 | |
Geothermal Energy Consumed by the Commercial Sector | Trillion Btu | EIA | 3 | |
Biomass Energy Consumed by the Commercial Sector | Trillion Btu | EIA | 3 | |
Total Renewable Energy Consumed by the Commercial Sector | Trillion Btu | EIA | 3 | |
Total Primary Energy Consumed by the Commercial Sector | Trillion Btu | EIA | 4 | |
Electricity Retail Sales to the Commercial Sector | Trillion Btu | EIA | 4 | |
Commercial Sector Electrical System Energy Losses | Trillion Btu | EIA | 4 | |
Total Energy Consumed by the Commercial Sector | Trillion Btu | EIA | 4 | |
Coal Consumed by the Electric Power Sector | Trillion Btu | EIA | 4 | |
Natural Gas Consumed by the Electric Power Sector (Excluding Supplemental Gaseous Fuels) | Trillion Btu | EIA | 4 | |
Petroleum Consumed by the Electric Power Sector | Trillion Btu | EIA | 4 | |
Total Fossil Fuels Consumed by the Electric Power Sector | Trillion Btu | EIA | 4 | |
Nuclear Electric Power Consumed by the Electric Power Sector | Trillion Btu | EIA | 4 | |
Conventional Hydroelectric Power Consumed by the Electric Power Sector | Trillion Btu | EIA | 4 | |
Geothermal Energy Consumed by the Electric Power Sector | Trillion Btu | EIA | 3 | |
Solar/PV Energy Consumed by the Electric Power Sector | Trillion Btu | EIA | 3 | |
Biomass Energy Consumed by the Electric Power Sector | Trillion Btu | EIA | 3 | |
Total Renewable Energy Consumed by the Electric Power Sector | Trillion Btu | EIA | 4 | |
Total Primary Energy Consumed by the Electric Power Sector | Trillion Btu | EIA | 4 | |
Natural Gas Consumed by the Residential Sector (Excluding Supplemental Gaseous Fuels) | Trillion Btu | EIA | 4 | |
Petroleum Consumed by the Residential Sector | Trillion Btu | EIA | 4 | |
Total Fossil Fuels Consumed by the Residential Sector | Trillion Btu | EIA | 4 | |
Geothermal Energy Consumed by the Residential Sector | Trillion Btu | EIA | 3 | |
Solar/PV Energy Consumed by the Residential Sector | Trillion Btu | EIA | 3 | |
Biomass Energy Consumed by the Residential Sector | Trillion Btu | EIA | 3 | |
Total Renewable Energy Consumed by the Residential Sector | Trillion Btu | EIA | 3 | |
Total Primary Energy Consumed by the Residential Sector | Trillion Btu | EIA | 4 | |
Electricity Retail Sales to the Residential Sector | Trillion Btu | EIA | 4 | |
Residential Sector Electrical System Energy Losses | Trillion Btu | EIA | 4 | |
Total Energy Consumed by the Residential Sector | Trillion Btu | EIA | 4 | |
Natural Gas Consumed by the Transportation Sector (Excluding Supplemental Gaseous Fuels) | Trillion Btu | EIA | 3 | |
Petroleum Consumed by the Transportation Sector (Excluding Biofuels) | Trillion Btu | EIA | 4 | |
Total Fossil Fuels Consumed by the Transportation Sector | Trillion Btu | EIA | 4 | |
Biomass Energy Consumed by the Transportation Sector | Trillion Btu | EIA | 3 | |
Total Primary Energy Consumed by the Transportation Sector | Trillion Btu | EIA | 4 | |
Electricity Retail Sales to the Transportation Sector | Trillion Btu | EIA | 3 | |
Transportation Sector Electrical System Energy Losses | Trillion Btu | EIA | 3 | |
Total Energy Consumed by the Transportation Sector | Trillion Btu | EIA | 4 | |
Crude Oil and Natural Gas Rotary Rigs in Operation, Onshore | Number of Rigs | EIA | 4 | Production |
Crude Oil and Natural Gas Rotary Rigs in Operation, Offshore | Number of Rigs | EIA | 4 | |
Crude Oil Rotary Rigs in Operation | Number of Rigs | EIA | 4 | |
Natural Gas Rotary Rigs in Operation | Number of Rigs | EIA | 4 | |
Crude Oil and Natural Gas Rotary Rigs in Operation, Total | Number of Rigs | EIA | 4 | |
Active Well Service Rig Count | Number of Rigs | EIA | 4 | |
Wells Drilled, Exploratory, Crude Oil | Number of Wells | EIA | 3 | |
Wells Drilled, Exploratory, Natural Gas | Number of Wells | EIA | 3 | |
Wells Drilled, Exploratory, Dry | Number of Wells | EIA | 4 | |
Wells Drilled, Exploratory, Total | Number of Wells | EIA | 4 | |
Wells Drilled, Development, Crude Oil | Number of Wells | EIA | 4 | |
Wells Drilled, Development, Natural Gas | Number of Wells | EIA | 4 | |
Wells Drilled, Development, Dry | Number of Wells | EIA | 4 | |
Wells Drilled, Development, Total | Number of Wells | EIA | 4 | |
Wells Drilled, Total, Crude Oil | Number of Wells | EIA | 4 | |
Wells Drilled, Total, Natural Gas | Number of Wells | EIA | 4 | |
Wells Drilled, Total, Dry | Number of Wells | EIA | 4 | |
Crude Oil, Natural Gas, and Dry Wells Drilled, Total | Number of Wells | EIA | 4 | |
Total Footage Drilled | Thousand Feet | EIA | 4 | |
Fuel Ethanol, Excluding Denaturant, Feedstock | Trillion Btu | EIA | 3 | Renewable |
Fuel Ethanol, Excluding Denaturant, Losses and Co-products | Trillion Btu | EIA | 3 | |
Fuel Ethanol Production | Trillion Btu | EIA | 4 | |
Fuel Ethanol Consumption | Trillion Btu | EIA | 4 | |
Conventional Hydroelectric Power Consumed by the Industrial Sector | Trillion Btu | EIA | 3 | |
Biomass Energy Consumed by the Industrial Sector | Trillion Btu | EIA | 4 | |
Total Renewable Energy Consumed by the Industrial Sector | Trillion Btu | EIA | 4 | |
Biofuels Production | Trillion Btu | EIA | 3 | |
Total Biomass Energy Production | Trillion Btu | EIA | 4 | |
Total Renewable Energy Production | Trillion Btu | EIA | 4 | |
Hydroelectric Power Consumption | Trillion Btu | EIA | 4 | |
Geothermal Energy Consumption | Trillion Btu | EIA | 3 | |
Solar/PV Energy Consumption | Trillion Btu | EIA | 3 | |
Wood Energy Consumption | Trillion Btu | EIA | 4 | |
Waste Energy Consumption | Trillion Btu | EIA | 3 | |
Biofuels Consumption | Trillion Btu | EIA | 3 | |
Total Biomass Energy Consumption | Trillion Btu | EIA | 4 | |
Total Renewable Energy Consumption | Trillion Btu | EIA | 4 | |
Asphalt and Road Oil Product Supplied | 1000 Barrels/Day | EIA | 4 | Supplied |
Aviation Gasoline Product Supplied | 1000 Barrels/Day | EIA | 3 | |
Distillate Fuel Oil Product Supplied | 1000 Barrels/Day | EIA | 4 | |
Jet Fuel Product Supplied | 1000 Barrels/Day | EIA | 4 | |
Propane/Propylene Product Supplied | 1000 Barrels/Day | EIA | 4 | |
Liquefied Petroleum Gases Product Supplied | 1000 Barrels/Day | EIA | 4 | |
Motor Gasoline Product Supplied | 1000 Barrels/Day | EIA | 4 | |
Residual Fuel Oil Product Supplied | 1000 Barrels/Day | EIA | 4 | |
Other Petroleum Products Supplied | 1000 Barrels/Day | EIA | 4 | |
Total Petroleum Products Supplied | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Imports, Total | 1000 Barrels/Day | EIA | 4 | Imports |
Distillate Fuel Oil Imports | 1000 Barrels/Day | EIA | 4 | |
Jet Fuel Imports | 1000 Barrels/Day | EIA | 4 | |
Propane/Propylene Imports | 1000 Barrels/Day | EIA | 4 | |
Liquefied Petroleum Gases Imports | 1000 Barrels/Day | EIA | 4 | |
Finished Motor Gasoline Imports | 1000 Barrels/Day | EIA | 4 | |
Residual Fuel Oil Imports | 1000 Barrels/Day | EIA | 4 | |
Other Petroleum Products Imports | 1000 Barrels/Day | EIA | 4 | |
Total Petroleum Imports | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Exports | 1000 Barrels/Day | EIA | 4 | Exports |
Petroleum Products Exports | 1000 Barrels/Day | EIA | 4 | |
Total Petroleum Exports | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Persian Gulf Nations | 1000 Barrels/Day | EIA | 4 | World Production |
Crude Oil Production, Canada | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, China | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Egypt | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Mexico | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Norway | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Russia | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, UK | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, USA | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Total Non-OPEC | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, World | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Algeria | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Angola | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Ecuador | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Iran | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Iraq | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Kuwait | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Libya | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Nigeria | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Qatar | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Saudi Arabia | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, United Arab Emirates | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Venezuela | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Production, Total OPEC | 1000 Barrels/Day | EIA | 4 | |
Crude Oil Stocks, SPR | Million Barrels | EIA | 4 | Stocks |
Crude Oil Stocks, Non-SPR | Million Barrels | EIA | 4 | |
Crude Oil Stocks, Total | Million Barrels | EIA | 4 | |
Distillate Fuel Oil Stocks | Million Barrels | EIA | 4 | |
Jet Fuel Stocks | Million Barrels | EIA | 3 | |
Propane/Propylene Stocks | Million Barrels | EIA | 3 | |
Liquefied Petroleum Gases Stocks | Million Barrels | EIA | 4 | |
Motor Gasoline Stocks (Including Blending Components and Gasohol) | Million Barrels | EIA | 4 | |
Residual Fuel Oil Stocks | Million Barrels | EIA | 3 | |
Other Petroleum Products Stocks | Million Barrels | EIA | 4 | |
Total Petroleum Stocks | Million Barrels | EIA | 4 | |
Crude Oil Refinery and Blender Net Input | 1000 Barrels/Day | EIA | 4 | Refinery |
Natural Gas Plant Liquids Refinery and Blender Net Inputs | 1000 Barrels/Day | EIA | 4 | |
Other Liquids Refinery and Blender Net Inputs | 1000 Barrels/Day | EIA | 4 | |
Total Petroleum Refinery and Blender Net Inputs | 1000 Barrels/Day | EIA | 4 | |
Distillate Fuel Oil Refinery and Blender Net Production | 1000 Barrels/Day | EIA | 4 | |
Jet Fuel Refinery and Blender Net Production | 1000 Barrels/Day | EIA | 4 | |
Propane/Propylene Refinery and Blender Net Production | 1000 Barrels/Day | EIA | 4 | |
Liquefied Petroleum Gases Refinery and Blender Net Production | 1000 Barrels/Day | EIA | 4 | |
Finished Motor Gasoline Refinery and Blender Net Production | 1000 Barrels/Day | EIA | 4 | |
Residual Fuel Oil Refinery and Blender Net Production | 1000 Barrels/Day | EIA | 4 | |
Other Petroleum Products Refinery and Blender Net Production | 1000 Barrels/Day | EIA | 4 | |
Total Petroleum Refinery and Blender Net Production | 1000 Barrels/Day | EIA | 4 | |
Petroleum Consumption, France | 1000 Barrels/Day | EIA | 4 | World Consumption |
Petroleum Consumption, Germany | 1000 Barrels/Day | EIA | 4 | |
Petroleum Consumption, Italy | 1000 Barrels/Day | EIA | 4 | |
Petroleum Consumption, UK | 1000 Barrels/Day | EIA | 4 | |
Petroleum Consumption, OECD Europe | 1000 Barrels/Day | EIA | 4 | |
Petroleum Consumption, Canada | 1000 Barrels/Day | EIA | 4 | |
Petroleum Consumption, Japan | 1000 Barrels/Day | EIA | 4 | |
Petroleum Consumption, South Korea | 1000 Barrels/Day | EIA | 4 | |
Petroleum Consumption, USA | 1000 Barrels/Day | EIA | 4 | |
Petroleum Consumption, Other OECD | 1000 Barrels/Day | EIA | 4 | |
Petroleum Consumption, Total OECD | 1000 Barrels/Day | EIA | 4 | |
Petroleum Stocks, France | Million Barrels | EIA | 4 | |
Petroleum Stocks, Germany | Million Barrels | EIA | 4 | |
Petroleum Stocks, Italy | Million Barrels | EIA | 4 | |
Petroleum Stocks, UK | Million Barrels | EIA | 4 | |
Petroleum Stocks, OECD Europe | Million Barrels | EIA | 4 | |
Petroleum Stocks, Canada | Million Barrels | EIA | 4 | |
Petroleum Stocks, Japan | Million Barrels | EIA | 4 | |
Petroleum Stocks, South Korea | Million Barrels | EIA | 4 | |
Petroleum Stocks, USA | Million Barrels | EIA | 4 | |
Petroleum Stocks, Other OECD | Million Barrels | EIA | 4 | |
Petroleum Stocks, Total OECD | Million Barrels | EIA | 4 | |
Yield on 10-year US treasury | Percent | DS | 3 | Financial |
US Money Supply M1 | Billion Dollars | DS | 3 | |
US Money Supply M2 | Billion Dollars | DS | 3 | |
US Prime Rate Charged by Banks (month average) | Percent | DS | 3 | |
US Capacity Utilization Rate—All Industry | Percent | DS | 3 | |
US Consumer Confidence Index | Index | DS | 3 | |
US PPI—Finished Goods | Index | DS | 3 | |
US PPI—Finished Goods Less Foods & Energy (core) | Index | DS | 3 | |
US Federal Funds Target Rate—Middle Rate | Percent | DS | 3 | |
US Chain-Type Price Index for Personal Consumption Expenditures | Index | DS | 3 | |
US CPI—All Urban All Items | Index | DS | 3 | |
US Industrial Production—Total Index | Index | DS | 3 | |
US New Private Housing units Started (AR) | Index | DS | 3 | |
US Treasury Yield Adjusted to Constant Maturity—20 Years | Percent | DS | 3 | |
US Dow Jones Industrials Share Price Index | Index | DS | 3 | |
S&P 500 Composite Price Index | Index | DS | 3 | |
US Treasury Yield Adjusted to Constant Maturity—3 Year | Percent | DS | 3 | |
Volume 1 MO | Index | DS | 3 | |
Open Interest 1 MO | Index | DS | 3 | |
Volume 6 MO | Index | DS | 3 | |
Open Interest 6 MO | Index | DS | 3 | |
Open Interest 12 MO | Index | DS | 3 | |
Volume 3MO | Index | DS | 3 | |
Open Interest 3 MO | Index | DS | 3 | |
Exxon Mobil | Dollars | DS | 3 | |
BP SPN.ADR 1:6 | Dollars | DS | 3 | |
Conoco Phillips | Dollars | DS | 3 | |
Royal Dutch Shell B | Dollars | DS | 3 | |
Chevron | Dollars | DS | 3 | |
Crude Oil-Dtd Brent UK Close U$/BBL | Dollars | DS | 3 | |
Crude Oil-Brent 1Mth Fwd FOB U$/BBL | Dollars | DS | 3 | |
US Treasury Bill Rate 3 Months | Percent | DS | 3 | |
US-DS Oil & Gas—Price Index | Index | DS | 3 | |
DAX 30 Performance Index | Index | DS | 3 | |
UK FTSE 100 Index | Index | DS | 3 | |
EK Industrial Production Excluding Construction | Index | DS | 3 | |
US $ TO UK £ (WMR)—Exchange Rate | Exchange Rate | DS | 3 | |
UK Index of Production Industries—All Production | Index | DS | 3 | |
DJGL World Industrials—Price Index | Index | DS | 3 | |
CBOE SPX Volatility Vix (new)—Price Index | Index | DS | 3 | |
NYM—Natural Gas Strip 1-month settled price | Dollars | DS | 3 | |
NYM-Natural Gas Strip M03—Settlement Price | Dollars | DS | 3 | |
NYM- Natural Gas Strip M06—Settlement Price | Dollars | DS | 3 | |
NYM-NY Heating Oil Strip M03—Settlement Price | Dollars | DS | 3 |
Appendix B: Graphs of the R2 of the individual explanatory variables and each of the five factors
See
,
,
,
Rights and permissions
About this article
Cite this article
Binder, K.E., Pourahmadi, M. & Mjelde, J.W. The role of temporal dependence in factor selection and forecasting oil prices. Empir Econ 58, 1185–1223 (2020). https://doi.org/10.1007/s00181-018-1574-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00181-018-1574-9