IDEAS home Printed from https://ideas.repec.org/p/zbw/sfb649/sfb649dp2015-041.html

Forecasting the oil price using house prices

Author

Listed:
  • Schulz, Rainer
  • Wersing, Martin

Abstract

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 explain these forecast improvements with the dominant role of the oil industry in Aberdeen. House prices aggregate the dispersed knowledge of the future oil price that exists in the city. We obtain similar empirical evidence for Houston, another city dominated by the oil industry. Consistent with our explanation, we find that house prices from economically more diversified areas in the UK and the US do not improve oil price forecasts.

Suggested Citation

  • Schulz, Rainer & Wersing, Martin, 2015. "Forecasting the oil price using house prices," SFB 649 Discussion Papers 2015-041, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2015-041
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/122014/1/833532960.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    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. 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.
    7. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
    8. repec:aen:journl:2009v30-02-a09 is not listed on IDEAS
    9. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:hum:wpaper:sfb649dp2015-041 is not listed on IDEAS
    2. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    3. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    4. Ravazzolo Francesco & Rothman Philip, 2016. "Oil-price density forecasts of US GDP," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 441-453, September.
    5. Czudaj, Robert L., 2022. "Heterogeneity of beliefs and information rigidity in the crude oil market: Evidence from survey data," European Economic Review, Elsevier, vol. 143(C).
    6. Liu, Donghui & Meng, Lingjie & Wang, Yudong, 2021. "The asymmetric effects of oil price changes on China’s exports: New evidence from a nonlinear autoregressive distributed lag model," Journal of Asian Economics, Elsevier, vol. 77(C).
    7. Apergis, Nicholas & Chatziantoniou, Ioannis & Cooray, Arusha, 2020. "Monetary policy and commodity markets: Unconventional versus conventional impact and the role of economic uncertainty," International Review of Financial Analysis, Elsevier, vol. 71(C).
    8. Christopher Thiem, 2018. "Oil price uncertainty and the business cycle: Accounting for the influences of global supply and demand within a VAR GARCH-in-mean framework," Applied Economics, Taylor & Francis Journals, vol. 50(34-35), pages 3735-3751, July.
    9. Atems, Bebonchu & Mette, Jehu & Lin, Guoyu & Madraki, Golshan, 2023. "Estimating and forecasting the impact of nonrenewable energy prices on US renewable energy consumption," Energy Policy, Elsevier, vol. 173(C).
    10. Eyal Dvir & Ken Rogoff, 2009. "The Three Epochs of Oil," Boston College Working Papers in Economics 706, Boston College Department of Economics.
    11. Dong, Minyi & Chang, Chun-Ping & Gong, Qiang & Chu, Yin, 2019. "Revisiting global economic activity and crude oil prices: A wavelet analysis," Economic Modelling, Elsevier, vol. 78(C), pages 134-149.
    12. Benk, Szilard & Gillman, Max, 2020. "Granger predictability of oil prices after the Great Recession," Journal of International Money and Finance, Elsevier, vol. 101(C).
    13. Salisu, Afees A. & Ogbonna, Ahamuefula E., 2019. "Another look at the energy-growth nexus: New insights from MIDAS regressions," Energy, Elsevier, vol. 174(C), pages 69-84.
    14. Wei, Yanfeng & Guo, Xiaoying, 2016. "An empirical analysis of the relationship between oil prices and the Chinese macro-economy," Energy Economics, Elsevier, vol. 56(C), pages 88-100.
    15. Sugra Ingilab Humbatova & Natig Qadim-Oglu Hajiyev, 2019. "Oil Factor in Economic Development," Energies, MDPI, vol. 12(8), pages 1-40, April.
    16. Benk, Szilard & Gillman, Max, 2020. "Granger predictability of oil prices after the Great Recession," Journal of International Money and Finance, Elsevier, vol. 101(C).
    17. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2019. "Large time‐varying parameter VARs: A nonparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1027-1049, November.
    18. Mustafa Kocoglu & Phouphet Kyophilavong & Ashar Awan & So Young Lim, 2023. "Time-varying causality between oil price and exchange rate in five ASEAN economies," Economic Change and Restructuring, Springer, vol. 56(2), pages 1007-1031, April.
    19. Reitz, Stefan & Ruelke, Jan & Stadtmann, Georg, 2009. "Are oil-price-forecasters finally right? -- Regressive expectations towards more fundamental values of the oil price," MPRA Paper 15607, University Library of Munich, Germany.
    20. Balcilar, Mehmet & Gupta, Rangan & Wohar, Mark E., 2017. "Common cycles and common trends in the stock and oil markets: Evidence from more than 150years of data," Energy Economics, Elsevier, vol. 61(C), pages 72-86.
    21. Li, Qiming & Cheng, Ke & Yang, Xiaoguang, 2017. "Response pattern of stock returns to international oil price shocks: From the perspective of China’s oil industrial chain," Applied Energy, Elsevier, vol. 185(P2), pages 1821-1831.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:sfb649:sfb649dp2015-041. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/sohubde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.