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Is a DFM Well Suited for Forecasting Regional House Price Inflation?

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Author Info

  • Sonali Das
  • Rangan Gupta
  • Alain Kabundi

Abstract

This paper uses the Dynamic Factor Model (DFM) framework, which accommodates a large cross-section of macroeconomic time series for forecasting regional house price inflation. As a case study, we use data on house price inflation for five metropolitan areas of South Africa. The DFM used in this study contains 282 quarterly series observed over the period 1980Q1-2006Q4. The results, based on the Mean Absolute Errors of one- to four-quarters-ahead out of sample forecasts over the period of 2001Q1 to 2006Q4, indicate that, in majority of the cases, the DFM outperforms the VARs, both classical and Bayesian, with the latter incorporating both spatial and non-spatial models. Our results, thus, indicate the blessing of dimensionality.

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Bibliographic Info

Paper provided by Economic Research Southern Africa in its series Working Papers with number 85.

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Date of creation: 2008
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Handle: RePEc:rza:wpaper:85

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Related research

Keywords: Dynamic Factor Model; VAR; BVAR; Forecast Accuracy;

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Cited by:
  1. Das, Sonali & Gupta, Rangan & Kabundi, Alain, 2009. "Could we have predicted the recent downturn in the South African housing market?," Journal of Housing Economics, Elsevier, vol. 18(4), pages 325-335, December.
  2. 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.
  3. Rangan Gupta & Marius Jurgilas & Alain Kabundi, 2009. "The Effect Of Monetary Policy On Real House Price Growth In South Africa: A Factor Augmented Vector Autoregression (Favar) Approach," Working Papers 200905, University of Pretoria, Department of Economics.

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