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Forecasting the oil price using house prices Mechanism and the Business Cycle

Listed author(s):
  • Rainer Schulz
  • Martin Wersing

We show that house prices from Aberdeen in the UK improve in- and out-of-sample oil price forecasts. The improvements are of a similar magnitude to those attained using macroeconomic indicators. We ex- plain these forecast improvements with the dominant role of the oil industry in Aberdeen. House prices aggregate the dispersed knowl- edge of the future oil price that exists in the city. We obtain similar empirical evidence for Houston, another city dominated by the oil in- dustry. Consistent with our explanation, we nd that house prices from economically more diversi ed areas in the UK and the US do not improve oil price forecasts.

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File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2015-041.pdf
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Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2015-041.

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Length: 59 pages
Date of creation: Aug 2015
Handle: RePEc:hum:wpaper:sfb649dp2015-041
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  1. C. Paul Hallwood, 1988. "Host Regions and the Globalization of the Offshore Oil Supply Industry: The Case of Aberdeen," International Regional Science Review, , vol. 11(2), pages 155-166, August.
  2. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
  3. Robert B. Barsky & Lutz Kilian, 2004. "Oil and the Macroeconomy Since the 1970s," Journal of Economic Perspectives, American Economic Association, vol. 18(4), pages 115-134, Fall.
  4. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
  5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
  6. Leslie Rosenthal, 2006. "Efficiency and Seasonality in the UK Housing Market, 1991-2001," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 68(3), pages 289-317, 06.
  7. James D. Hamilton, 2009. "Understanding Crude Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 179-206.
  8. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
  9. Malpezzi, Stephen & Maclennan, Duncan, 2001. "The Long-Run Price Elasticity of Supply of New Residential Construction in the United States and the United Kingdom," Journal of Housing Economics, Elsevier, vol. 10(3), pages 278-306, September.
  10. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
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