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Forecasting the US Real House Price Index: Structural and Non-Structural Models with and without Fundamentals

  • Rangan Gupta

    ()

    (Department of Economics, University of Pretoria)

  • Alain Kabundi

    ()

    (Department of Economics and Econometrics, University of Johannesburg)

  • Stephen M. Miller

    ()

    (College of Business, University of Las Vegas, Nevada)

We employ a 10-variable dynamic structural general equilibrium model to forecast the US real house price index as well as its turning point in 2006:Q2. We also examine various Bayesian and classical time-series models in our forecasting exercise to compare to the dynamic stochastic general equilibrium model, estimated using Bayesian methods. In addition to standard vector-autoregressive and Bayesian vector autoregressive models, we also include the information content of either 10 or 120 quarterly series in some models to capture the influence of fundamentals. We consider two approaches for including information from large data sets – extracting common factors (principle components) in a Factor-Augmented Vector Autoregressive or Factor-Augmented Bayesian Vector Autoregressive models or Bayesian shrinkage in a large-scale Bayesian Vector Autoregressive models. We compare the out-ofsample forecast performance of the alternative models, using the average root mean squared error for the forecasts. We find that the small-scale Bayesian-shrinkage model (10 variables) outperforms the other models, including the large-scale Bayesian-shrinkage model (120 variables). Finally, we use each model to forecast the turning point in 2006:Q2, using the estimated model through 2005:Q2. Only the dynamic stochastic general equilibrium model actually forecasts a turning point with any accuracy, suggesting that attention to developing forward-looking microfounded dynamic stochastic general equilibrium models of the housing market, over and above fundamentals, proves crucial in forecasting turning points.

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Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 200927.

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Length: 36 pages
Date of creation: Dec 2009
Date of revision:
Handle: RePEc:pre:wpaper:200927
Contact details of provider: Postal: PRETORIA, 0002
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Web page: http://www.up.ac.za/economics

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  1. Spencer, David E., 1993. "Developing a Bayesian vector autoregression forecasting model," International Journal of Forecasting, Elsevier, vol. 9(3), pages 407-421, November.
  2. Rangan Gupta & Stephen M. Miller, 2009. "The Time-Series Properties on Housing Prices: A Case Study of the Southern California Market," Working papers 2009-10, University of Connecticut, Department of Economics, revised Dec 2009.
  3. Rangan Gupta & Stephen M. Miller, 2009. ""Ripple Effects" and Forecasting Home Prices in Los Angeles, Las Vegas, and Phoenix," Working papers 2009-05, University of Connecticut, Department of Economics, revised Jun 2009.
  4. Rangan Gupta & Alain Kabundi & Stephen M. Miller, 2009. "Using Large Data Sets to Forecast Housing Prices: A Case Study of Twenty US States," Working papers 2009-13, University of Connecticut, Department of Economics.
  5. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
  6. Forni, Mario, et al, 2001. "Coincident and Leading Indicators for the Euro Area," Economic Journal, Royal Economic Society, vol. 111(471), pages C62-85, May.
  7. Rapach, David E. & Strauss, Jack K., 2009. "Differences in housing price forecastability across US states," International Journal of Forecasting, Elsevier, vol. 25(2), pages 351-372.
  8. Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May.
  9. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2006. "Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components?," Discussion Paper Series 1: Economic Studies 2006,32, Deutsche Bundesbank, Research Centre.
  10. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
  11. Bernanke, Ben & Gertler, Mark, 1995. "Inside the Black Box: The Credit Channel of Monetary Policy Transmission," Working Papers 95-15, C.V. Starr Center for Applied Economics, New York University.
  12. Kapetanios, George & Marcellino, Massimiliano, 2006. "A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions," CEPR Discussion Papers 5620, C.E.P.R. Discussion Papers.
  13. Gupta, Rangan & Kabundi, Alain, 2011. "A large factor model for forecasting macroeconomic variables in South Africa," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1076-1088, October.
  14. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2002. "Do Financial Variables Help Forecasting Inflation and Real Activity in the Euro Area?," CEPR Discussion Papers 3146, C.E.P.R. Discussion Papers.
  15. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro-area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
  16. Geetesh Bhardwaj & Norman Swanson, 2004. "An Empirical Investigation of the Usefulness of ARFIMA Models for Predicting Macroeconomic and Financial Time Series," Departmental Working Papers 200422, Rutgers University, Department of Economics.
  17. Riccardo Cristadoro & Mario Forni & Lucrezia Reichlin & Giovanni Veronese, 2005. "A core inflation indicator for the Euro area," ULB Institutional Repository 2013/10131, ULB -- Universite Libre de Bruxelles.
  18. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409 National Bureau of Economic Research, Inc.
  19. James H. Stock & Mark W. Watson, 2001. "Forecasting output and inflation: the role of asset prices," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
  20. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
  21. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
  22. Boivin, Jean & Giannoni, Marc P. & Mihov, Ilian, 2006. "Sticky prices and monetary policy: Evidence from disaggregated US data," CFS Working Paper Series 2007/14, Center for Financial Studies (CFS).
  23. Robert Barsky & Christopher L. House & Miles Kimball, 2005. "Sticky Price Models and Durable Goods," Macroeconomics 0501031, EconWPA.
  24. Domenico Giannone & Troy Matheson, 2007. "A new core inflation indicator for New Zealand," ULB Institutional Repository 2013/6407, ULB -- Universite Libre de Bruxelles.
  25. Todd E. Clark & Michael W. McCracken, 1999. "Tests of equal forecast accuracy and encompassing for nested models," Research Working Paper 99-11, Federal Reserve Bank of Kansas City.
  26. Rangan Gupta & Sonali Das, 2008. "Predicting Downturns in the US Housing Market: A Bayesian Approach," Working Papers 200821, University of Pretoria, Department of Economics.
  27. Rangan Gupta & Sonali Das, 2008. "Spatial Bayesian Methods Of Forecasting House Prices In Six Metropolitan Areas Of South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 76(2), pages 298-313, 06.
  28. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
  29. Rangan Gupta, 2006. "FORECASTING THE SOUTH AFRICAN ECONOMY WITH VARs AND VECMs," South African Journal of Economics, Economic Society of South Africa, vol. 74(4), pages 611-628, December.
  30. Alain N. Kabundi, 2004. "Estimation of Economic Growth in France Using Business Survey Data," IMF Working Papers 04/69, International Monetary Fund.
  31. Matteo Iacoviello & Stefano Neri, 2008. "Housing market spillovers : evidence from an estimated DSGE model," Working Paper Research 145, National Bank of Belgium.
  32. Zellner, Arnold, 1986. "A tale of forecasting 1001 series : The Bayesian knight strikes again," International Journal of Forecasting, Elsevier, vol. 2(4), pages 491-494.
  33. Martha Banbura & Domenico Giannone & Lucrezia Reichlin, 2008. "Large Bayesian VARs," Working Papers ECARES 2008_033, ULB -- Universite Libre de Bruxelles.
  34. Quigley, John M., 2002. "Real Estate Prices and Economic Cycles," Berkeley Program on Housing and Urban Policy, Working Paper Series qt58c6v2kx, Berkeley Program on Housing and Urban Policy.
  35. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
  36. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach," Finance and Economics Discussion Series 2004-03, Board of Governors of the Federal Reserve System (U.S.).
  37. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1986. "Forecasting and conditional projection using realistic prior distribution," Staff Report 93, Federal Reserve Bank of Minneapolis.
  38. Dua, Pami & Miller, Stephen M & Smyth, David J, 1999. "Using Leading Indicators to Forecast U.S. Home Sales in a Bayesian Vector Autoregressive Framework," The Journal of Real Estate Finance and Economics, Springer, vol. 18(2), pages 191-205, March.
  39. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
  40. Chris Bloor & Troy Matheson, 2008. "Analysing shock transmission in a data-rich environment: A large BVAR for New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2008/09, Reserve Bank of New Zealand.
  41. Elena Angelini & Jérôme Henry & Ricardo Mestre, 2001. "Diffusion index-based inflation forecasts for the euro area," BIS Papers chapters, in: Bank for International Settlements (ed.), Empirical studies of structural changes and inflation, volume 3, pages 109-138 Bank for International Settlements.
  42. Ben S. Bernanke & Jean Boivin, 2001. "Monetary Policy in a Data-Rich Environment," NBER Working Papers 8379, National Bureau of Economic Research, Inc.
  43. Zellner, Arnold & Palm, Franz, 1974. "Time series analysis and simultaneous equation econometric models," Journal of Econometrics, Elsevier, vol. 2(1), pages 17-54, May.
  44. Rangan Gupta & Moses M. Sichei, 2006. "A BVAR Model for the South African Economy," Working Papers 200612, University of Pretoria, Department of Economics.
  45. Thomas Hyclak & Geraint Johnes, 1999. "original: House prices and regional labor markets," The Annals of Regional Science, Springer, vol. 33(1), pages 33-49.
  46. 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.
  47. 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.
  48. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  49. Rangan Gupta & Alain Kabundi, 2009. "Forecasting Real Us House Price: Principal Components Versus Bayesian Regressions," Working Papers 200907, University of Pretoria, Department of Economics.
  50. Marc-André Gosselin & Greg Tkacz, 2001. "Evaluating Factor Models: An Application to Forecasting Inflation in Canada," Working Papers 01-18, Bank of Canada.
  51. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  52. Gary Chamberlain & Michael Rothschild, 1982. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," NBER Working Papers 0996, National Bureau of Economic Research, Inc.
  53. Sonali Das & Rangan Gupta & Alain Kabundi, 2008. "Could We Have Predicted The Recent Downturn In The South African Housing Market?," Working Papers 200831, University of Pretoria, Department of Economics.
  54. Richard M. Todd, 1984. "Improving economic forecasting with Bayesian vector autoregression," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
  55. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
  56. Geraint Johnes & Thomas Hyclak, . "House Prices and Regional Labor Markets," Working Papers ec15/93, Department of Economics, University of Lancaster.
  57. Christian Schumacher & Christian Dreger, 2004. "Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models?," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 224(6), pages 731-750, November.
  58. Topel, Robert H & Rosen, Sherwin, 1988. "Housing Investment in the United States," Journal of Political Economy, University of Chicago Press, vol. 96(4), pages 718-40, August.
  59. Chamberlain, Gary, 1983. "Funds, Factors, and Diversification in Arbitrage Pricing Models," Econometrica, Econometric Society, vol. 51(5), pages 1305-23, September.
  60. Wang, Mu-Chun, 2008. "Comparing the DSGE model with the factor model: an out-of-sample forecasting experiment," Discussion Paper Series 1: Economic Studies 2008,04, Deutsche Bundesbank, Research Centre.
  61. Sonali Das & Rangan Gupta & Alain Kabundi, 2011. "Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(2), pages 288-302, March.
  62. Martin Schneider & Martin Spitzer, 2004. "Forecasting Austrian GDP using the generalized dynamic factor model," Working Papers 89, Oesterreichische Nationalbank (Austrian Central Bank).
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