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Using Leading Indicators to Forecast US Home Sales in a Bayesian VAR Framework

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Author Info

  • Pami Dua

    (University of Connecticut)

  • Stephen M. Miller

    (University of Connecticut)

  • David J. Smyth

    (Louisiana State University)

Abstract

This paper uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting US home sales. The benchmark Bayesian model includes home sales, the price of homes, the mortgage rate, real personal disposable income, and the unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.

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File URL: http://www.econ.uconn.edu/working/1996-08.pdf
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Bibliographic Info

Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 1996-08.

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Length: 22 pages
Date of creation: Nov 1996
Date of revision:
Publication status: Published in Journal of Real Estate Finance and Economics, March 1999
Handle: RePEc:uct:uconnp:1996-08

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Postal: University of Connecticut 341 Mansfield Road, Unit 1063 Storrs, CT 06269-1063
Phone: (860) 486-4889
Fax: (860) 486-4463
Web page: http://www.econ.uconn.edu/
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References

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  1. Richard M. Todd, 1984. "Improving economic forecasting with Bayesian vector autoregression," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
  2. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  3. Engle, Robert F. & Yoo, Byung Sam, 1987. "Forecasting and testing in co-integrated systems," Journal of Econometrics, Elsevier, vol. 35(1), pages 143-159, May.
  4. Zellner, Arnold & Palm, Franz, 1974. "Time series analysis and simultaneous equation econometric models," Journal of Econometrics, Elsevier, vol. 2(1), pages 17-54, May.
  5. Isaac F. Megbolugbe & Allen P. Marks & Mary B. Schwartz, 1991. "The Economic Theory of Housing Demand: A Critical Review," Journal of Real Estate Research, American Real Estate Society, vol. 6(3), pages 381-393.
  6. Christopher A. Sims, 1988. "Bayesian skepticism on unit root econometrics," Discussion Paper / Institute for Empirical Macroeconomics 3, Federal Reserve Bank of Minneapolis.
  7. Cooley, Thomas F. & Leroy, Stephen F., 1985. "Atheoretical macroeconometrics: A critique," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 283-308, November.
  8. Robert B. Litterman, 1985. "Forecasting with Bayesian vector autoregressions five years of experience," Working Papers 274, Federal Reserve Bank of Minneapolis.
  9. Robert B. Litterman, 1984. "Above-average national growth in 1985 and 1986," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
  10. Arnott, Richard, 1987. "Economic theory and housing," Handbook of Regional and Urban Economics, in: E. S. Mills (ed.), Handbook of Regional and Urban Economics, edition 1, volume 2, chapter 24, pages 959-988 Elsevier.
  11. Dua, Pami & Miller, Stephen M, 1996. "Forecasting Connecticut Home Sales in a BVAR Framework Using Coincident and Leading Indexes," The Journal of Real Estate Finance and Economics, Springer, vol. 13(3), pages 219-35, November.
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Cited by:
  1. Rangan Gupta & Stephen M. Miller, 2009. "The Time-Series Properties of House Prices: A Case Study of the Southern California Market," Working Papers 0912, University of Nevada, Las Vegas , Department of Economics, revised Dec 2009.

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