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

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Author Info

  • 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)

Abstract

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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Bibliographic Info

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

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Keywords: US House prices; Forecasting; DSGE models; Factor Augmented Models; Large-Scale BVAR models;

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References

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Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. DSGE models and forecasting
    by Christian Zimmermann in NEP-DGE blog on 2009-12-21 00:35:25
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Cited by:
  1. Philipp an de Meulen & Martin Micheli & Torsten Schmidt, 2011. "Forecasting House Prices in Germany," Ruhr Economic Papers 0294, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  2. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working papers 2012-38, University of Connecticut, Department of Economics, revised Dec 2013.
  3. Sarah Drought & Chris McDonald, 2011. "Forecasting house price inflation: a model combination approach," Reserve Bank of New Zealand Discussion Paper Series DP2011/07, Reserve Bank of New Zealand.
  4. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2014. "A Two Stage Approach to Spatiotemporal Analysis with Strong and Weak Cross-Sectional Dependence," CESifo Working Paper Series 4592, CESifo Group Munich.
  5. Rangan Gupta & Marius Jurgilas & Stephen M. Miller & Dylan van Wyk, 2010. "Financial Market Liberalization, Monetary Policy, and Housing Price Dynamics," Working Papers 201009, University of Pretoria, Department of Economics.
  6. Rangan Gupta, 2012. "Forecasting House Prices for the Four Census Regions and the Aggregate US Economy: The Role of a Data-Rich Environment," Working Papers 201214, University of Pretoria, Department of Economics.

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