The 47 references in paper Christiane Baumeister, Lutz Kilian (2011) “Real-Time Forecasts of the Real Price of Oil” / RePEc:bca:bocawp:11-16

1
Alquist, R., and L. Kilian (2010), “What Do We Learn from the Price of Crude Oil Futures?” Journal of Applied Econometrics, 25, 539-573.
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2
Alquist, R., Kilian, L., and R.J. Vigfusson (2011), “Forecasting the Price of Oil,” prepared for: G. Elliott and A. Timmermann (eds.), Handbook of Economic Forecasting, 2, Amsterdam: North-Holland.
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3
Baumeister, C., and G. Peersman (2010), “Sources of the Volatility Puzzle in the Crude Oil Market,” mimeo, Department of Economics, Ghent University.
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4
Carlton, A.B. (2010), “Oil Prices and Real-Time Output Growth,” mimeo, Department of Economics, University of Houston.
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5
Clark, T.E., and M. McCracken (2009), “Tests of Equal Predictive Ability with Real-Time Data,” Journal of Business and Economic Statistics, 27, 441-454.
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6
Clements, M.P., and A.B. Galvão (2010), “Real-Time Forecasting of Inflation and Output Growth in the Presence of Data Revisions,” mimeo, Department of Economics, Queen Mary University of London.
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7
Croushore, D. (2006), “Forecasting with Real-Time Macroeconomic Data,” in: G. Elliott, C.W.J. Granger and A. Timmermann (eds.), Handbook of Economic Forecasting, Amsterdam: North-Holland, 961-982.
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8
Croushore, D. (2011), “Frontiers of Real-Time Data Analysis,” Journal of Economic Literature, 49, 72-100.
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9
De Jong, P. (1987),”Rational Economic Data Revisions,” Journal of Business and Economic Statistics, 5, 539-548.
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10
Diebold, F.X., and R.S. Mariano (1995), “Comparing Predictive Accuracy,” Journal of Business and Economic Statistics, 13, 253-263.
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11
Faust, J., Rogers, J., and J.H. Wright (2003), “Exchange Rate Forecasting: The Errors We’ve Really Made,” Journal of International Economics, 60, 35-59.
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12
Faust, J., Rogers, J., and J.H. Wright (2005), “News and Noise in G-7 GDP Announcements,” Journal of Money, Credit, and Banking, 37, 403-419.
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13
Giannone, D., Lenza, M. and G. Primiceri (2010), “Prior Selection for Vector Autoregressions,” mimeo, Department of Economics, Free University of Brussels.
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14
Giannone, D., and L. Reichlin (2006), “Does Information Help Recover Structural Shocks from Past Observations,” Journal of the European Economic Association, 4, 455-465.
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15
Kilian, L. (2008), “Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy?” Review of Economics and Statistics, 90, 216-240.
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16
Kilian, L. (2009), “Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market,” American Economic Review, 99, 1053-1069.
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17
Kilian, L., and B. Hicks (2010), “Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003-2008?” mimeo, Department of Economics, University of Michigan.
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18
Kilian, L., and D. Murphy (2010), “The Role of Inventories and Speculative Trading in the Global Market for Crude Oil,” mimeo, University of Michigan.
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19
Kilian, L., and D. Murphy (2011), “Why Agnostic Sign Restrictions Are Not Enough: Understanding the Dynamics of Oil Market VAR Models,” forthcoming: Journal of the European Economic Association.
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20
Kilian, L., and C. Vega (2011), “Do Energy Prices Respond to U.S. Macroeconomic News? A Test of the Hypothesis of Predetermined Energy Prices,” Review of Economics and Statistics, 93, 660-671.
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21
Koenig, E.F., Dolmas, S., and J. Piger (2003), “The Use and Abuse of Real-Time Data in Economic Forecasting,” Review of Economics and Statistics, 85, 618-628.
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22
Mork, K.A. (1989), “Oil and the Macroeconomy. When Prices Go Up and Down: An Extension of Hamilton’s Results,” Journal of Political Economy, 97, 740-744.
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23
Pesaran, M.H., and A. Timmermann (1995), “Predictability of Stock Returns: Robustness and Economic Significance,” Journal of Finance, 50, 1201-1228.
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24
Pesaran, M.H., and A. Timmermann (2009), “Testing Dependence Among Serially Correlated Multicategory Variables,” Journal of the American Statistical Association, 104, 325 337.
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25
Ravazzolo, F., and P. Rothman (2010), “Oil and U.S. GDP: A Real-Time Out-of-Sample Examination,” mimeo, Norges Bank.
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26
Waggoner, D.F., and T. Zha (1999), “Conditional Forecasts in Dynamic Multivariate Models,” Review of Economics and Statistics, 81, 639-651.
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27
Exhibit 1: Descriptive Statistics on Data Revisions
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(a) U.S. Refiners’ Acquisition Cost of Crude Oil Imports Mean Std. Dev. relative to expost data In-report revision -2.90×10-3 0.733×10-3 Post-report revision 0 0 Number of observations 237 (1990.10–2010.06) Vintage t reports observation up to 1991.01–2005.07: t-3 2005.08–2010.12: t-2 Average number of revisions 1.21
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(b) World Crude Oil Production Mean Std. Dev. relative to expost data In-report revision -0.86×10-3 0.976×10-3 Post-report revision -0.41×10-3 0.729×10-3
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Number of observations 237 (1990.10–2010.06) Vintage t reports observation up to t-3
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Average number of revisions 8.59
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(c) U.S. Crude Oil Inventories Mean Std. Dev. relative to expost data In-report revision -0.33×10-3 0.074×10-3 Post-report revision -0.02×10-3 0.019×10-3 Number of observations 235 (1990.12–2010.06) Vintage t reports observation up to t-1 Average number of revisions 1.54
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(d) U.S. Petroleum Inventories Mean Std. Dev. relative to expost data In-report revision 3.30×10-3 0.077×10-3 Post-report revision -0.01×10-3 0.002 ×10-3
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34
Number of observations 235 (1990.12–2010.06) Vintage t reports observation up to t-1
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Average number of revisions 1.74
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(e) OECD Petroleum Inventories Mean Std. Dev. relative to expost data In-report revision -0.77×10-3 0.048×10-3 Post-report revision -0.57×10-3 0.033×10-3 Number of observations 240 (1990.07–2010.06) Vintage t reports observation up to t-6 Average number of revisions 7.60
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(f) U.S. CPI Mean Std. Dev. relative to expost data In-report revision -0.35×10-3 0.044×10-3 Post-report revision 0 0
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38
Number of observations 235 (1990.12–2010.06) Vintage t reports observation up to t-1
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Average number of revisions 0.66
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Libyan Production Shortfall + Contagion 1 Figure 3: Forecast Scenarios for Real Refiners’ Acquisition Cost Percent Deviations from Baseline Forecast
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Iraq at Full Capacity 20 0 -20 03691215182124 Libyan Production Shortfall 20 0 -20 03691215182124 Contagion 1 20 0 -20 03691215182124 Contagion 2 20 0 -20 03691215182124 20 0 -20 03691215182124
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NOTES: A description of each scenario can be found in section 5.
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Figure 4: Forecast Scenarios for Real Refiners’ Acquisition Cost Percent Deviations from Baseline Forecast
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Global Recovery 100 80 60 40 20 0 03691215182124 Nightm are 100
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Nightmare 1 Nightmare 2 80 60 40 20 0
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NOTES: The two nightmare scenarios combine the global recovery scenario with the Libyan production shortfall scenario and with the contagion 1 and contagion 2 scenarios, respectively.
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48
2010.12 dollars2010.12 dollars
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