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

Author

Listed:
  • 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.

Suggested Citation

  • Pami Dua & Stephen M. Miller & David J. Smyth, 1996. "Using Leading Indicators to Forecast US Home Sales in a Bayesian VAR Framework," Working papers 1996-08, University of Connecticut, Department of Economics.
  • Handle: RePEc:uct:uconnp:1996-08
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    File URL: http://web2.uconn.edu/economics/working/1996-08.pdf
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    References listed on IDEAS

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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., 1988. "Bayesian skepticism on unit root econometrics," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 463-474.
    3. Robert B. Litterman, 1984. "Above-average national growth in 1985 and 1986," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
    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. 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.
    6. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    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. Smith, Lawrence B & Rosen, Kenneth T & Fallis, George, 1988. "Recent Developments in Economic Models of Housing Markets," Journal of Economic Literature, American Economic Association, vol. 26(1), pages 29-64, March.
    9. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    10. 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.
    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 219-235, November.
    12. 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.
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    Cited by:

    1. Rangan Gupta & Stephen Miller, 2012. "The Time-Series Properties of House Prices: A Case Study of the Southern California Market," The Journal of Real Estate Finance and Economics, Springer, vol. 44(3), pages 339-361, April.

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