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US Disposable Personal Income and Housing Price Index: A Fractional Integration Analysis

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  • Guglielmo Maria Caporale

    ()
    (Brunel University London)

  • Luis A. Gil-Alana

    ()
    (University of Navarra)

Abstract

This paper examines the relationship between US disposable personal income (DPI) and house price index (HPI) during the last twenty years applying fractional integration and long-range dependence techniques to monthly data from January 1991 to July 2010. The empirical findings indicate that the stochastic properties of the two series are such that cointegration cannot hold between them, as mean reversion occurs in the case of DPI but not of HPI. Also, recursive analysis shows that the estimated fractional parameter is relatively stable over time for DPI whilst it increases throughout the sample for HPI. Interestingly, the estimates tend to converge toward the unit root case after 2008 once the bubble had burst. The implications for explaining the recent financial crisis and choosing appropriate policy actions are discussed.

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Bibliographic Info

Paper provided by School of Economics and Business Administration, University of Navarra in its series Faculty Working Papers with number 03/11.

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Length: 22 pages
Date of creation: 18 Jan 2011
Date of revision:
Handle: RePEc:una:unccee:wp0311

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Web page: http://www.unav.es/facultad/econom

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  1. Lobato, I.N. & Savin, N.E., 1996. "Real and Spurious Long Memory Properties of Stock Market Data," Working Papers 96-07, University of Iowa, Department of Economics.
  2. Katsumi Shimotsu & Peter C.B. Phillips, 2002. "Exact Local Whittle Estimation of Fractional Integration," Economics Discussion Papers 535, University of Essex, Department of Economics.
  3. Carstensen, Kai, 2006. "Stock Market Downswing and the Stability of European Monetary Union Money Demand," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 395-402, October.
  4. Ben S. Bernanke & Mark Gertler, 2001. "Should Central Banks Respond to Movements in Asset Prices?," American Economic Review, American Economic Association, vol. 91(2), pages 253-257, May.
  5. Diebold, Francis X. & Rudebusch, Glenn D., 1989. "Long memory and persistence in aggregate output," Journal of Monetary Economics, Elsevier, vol. 24(2), pages 189-209, September.
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  7. Ben Bernanke & Kenneth N. Kuttner, 2003. "What explains the stock market's reaction to Federal Reserve policy?," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  8. Jonathan McCarthy & Richard W. Peach, 2004. "Are home prices the next "bubble"?," Economic Policy Review, Federal Reserve Bank of New York, issue Dec, pages 1-17.
  9. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
  10. Lobato, Ignacio N & Savin, N E, 1998. "Real and Spurious Long-Memory Properties of Stock-Market Data: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 280-83, July.
  11. Gil-Alana, L. A. & Robinson, P. M., 1997. "Testing of unit root and other nonstationary hypotheses in macroeconomic time series," Journal of Econometrics, Elsevier, vol. 80(2), pages 241-268, October.
  12. Meen, Geoffrey, 2002. "The Time-Series Behavior of House Prices: A Transatlantic Divide?," Journal of Housing Economics, Elsevier, vol. 11(1), pages 1-23, March.
  13. Katsumi Shimotsu & Peter C.B. Phillips, 2000. "Local Whittle Estimation in Nonstationary and Unit Root Cases," Cowles Foundation Discussion Papers 1266, Cowles Foundation for Research in Economics, Yale University, revised Sep 2003.
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