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The Blessing Of Dimensionality In Forecasting Real House Price Growth In The Nine Census Divisions Of The Us

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Author Info

  • Sonali Das

    ()
    (CSIR, Pretoria)

  • Rangan Gupta

    ()
    (Department of Economics, University of Pretoria)

  • Alain Kabundi

    ()
    (Department of Economics and Econometrics, University of Johannesburg)

Abstract

This paper analyzes whether a wealth of information contained in 126 monthly series used by large-scale Bayesian Vector Autoregressive (LBVAR) models, as well as Factor Augmented Vector Autoregressive (FAVAR) models, either Bayesian or classical, can prove to be more useful in forecasting real house price growth rate of the nine census divisions of the US, compared to the small-scale VAR models, that merely use the house prices. Using the period of 1991:02 to 2000:12 as the in-sample period and 2001:01 to 2005:06 as the out-of-sample horizon, we compare the forecast performance of the alternative models for one- to twelve–months ahead forecasts. Based on the average Root Mean Squared Error (RMSEs) for one- to twelve–months ahead forecasts, we find that the alternative FAVAR models outperform the other models in eight of the nine census divisions.

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Bibliographic Info

Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 200902.

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Length: 21 pages
Date of creation: Jan 2009
Date of revision:
Handle: RePEc:pre:wpaper:200902

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Keywords: Dynamic Factor Model; BVAR; Forecast Accuracy;

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Cited by:
  1. 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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