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Forecasting the US real house price index: Structural and non-structural models with and without fundamentals

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  • Gupta, Rangan
  • Kabundi, Alain
  • Miller, Stephen M.

Abstract

We employ a 10-variable dynamic structural general equilibrium model to forecast the US real house price index as well as its downturn 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 factor-augmented vector autoregressive or Bayesian factor-augmented vector autoregressive models as well as Bayesian shrinkage in a large-scale Bayesian vector autoregressive model. We compare the out-of-sample 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). In addition, when we use simple average forecast combinations, the combination forecast using the 10 best atheoretical models produces the minimum RMSEs compared to each of the individual models, followed closely by the combination forecast using the 10 atheoretical models and the DSGE model. Finally, we use each model to forecast the downturn point in 2006:Q2, using the estimated model through 2005:Q2. Only the dynamic stochastic general equilibrium model actually forecasts a downturn with any accuracy, suggesting that forward-looking microfounded dynamic stochastic general equilibrium models of the housing market may prove crucial in forecasting turning points.

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Bibliographic Info

Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 28 (2011)
Issue (Month): 4 (July)
Pages: 2013-2021

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Handle: RePEc:eee:ecmode:v:28:y:2011:i:4:p:2013-2021

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Web page: http://www.elsevier.com/locate/inca/30411

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Keywords: US House prices Forecasting DSGE models Factor augmented models Large-scale BVAR models;

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Blog mentions

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  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. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 201230, University of Pretoria, Department of Economics.
  2. Vasilios Plakandaras & Rangan Gupta & Periklis Gogas & Theophilos Papadimitriou, 2014. "Forecasting the U.S. Real House Price Index," Working Papers 201418, University of Pretoria, Department of Economics.
  3. 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.
  4. 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.
  5. Zietz, Joachim & Traian, Anca, 2014. "When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 271-281.
  6. 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.
  7. Rangan Gupta & Alain Kabundi & Stephen M. Miller & Josine Uwilingiye, 2011. "Using Large Data Sets to Forecast Sectoral Employment," Working papers 2011-02, University of Connecticut, Department of Economics, revised Aug 2012.
  8. 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.
  9. 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.

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