English, Article, Journal or magazine article edition: Using Large Data Sets to Forecast Housing Prices: A Case Study of Twenty US States Rangan Gupta; Alain Kabundi; Stephen M. Miller

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Bookmark: http://trove.nla.gov.au/version/183503
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Using Large Data Sets to Forecast Housing Prices: A Case Study of Twenty US States
Author
  • Rangan Gupta
  • Alain Kabundi
  • Stephen M. Miller
Physical Description
  • preprint
Notes
  • RePEc:pre:wpaper:200912
  • RePEc:uct:uconnp:2009-13
  • We implement several Bayesian and classical models to forecast housing prices in 20 US states. In addition to standard vector-autoregressive (VAR) and Bayesian vector autoregressive (BVAR) models, we also include the information content of 308 additional quarterly series in some models. Several approaches exist for incorporating information from a large number of series. We consider two approaches – extracting common factors (principle components) in a Factor-Augmented Vector Autoregressive (FAVAR) or Factor-Augmented Bayesian Vector Autoregressive (FABVAR) models or Bayesian shrinkage in a large-scale Bayesian Vector Autoregressive (LBVAR) models. In addition, we also introduce spatial or causality priors to augment the forecasting models. Using the period of 1976:Q1 to 1994:Q4 as the in-sample period and 1995:Q1 to 2003:Q4 as the out-of-sample horizon, we compare the forecast performance of the alternative models. Based on the average root mean squared error (RMSE) for the one-, two-, three-, and four–quarters-ahead forecasts, we find that one of the factor-augmented models generally outperform the large-scale models in the 20 US states examined in this paper.
  • Housing Prices, Forecasting, Factor Augmented Models, Large-Scale BVAR models
  • RePEc:nlv:wpaper:0916
Language
  • English
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