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Using Large Data Sets to Forecast Housing Prices: A Case Study of Twenty US States Author info | Abstract | Publisher info | Download info | Related research | Statistics 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)
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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.
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Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number
200912.
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Length: 33 pages
Date of creation: May 2009Date of revision:
Handle: RePEc:pre:wpaper:200912Contact details of provider: Postal: PRETORIA, 0002 Phone: (+2712) 420 2413 Fax: (+2712) 362-5207 Web page: http://web.up.ac.za/default.asp?ipkCategoryID=40 More information through EDIRC
For technical questions regarding this item, or to correct its listing, contact: (Rangan Gupta).
Keywords: Housing prices ; Forecasting ; Factor Augmented Models ; Large-Scale BVAR models ; Other versions of this item:
Find related papers by JEL classification: C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions R31 - Urban, Rural, and Regional Economics - - Production Analysis and Firm Location - - - Housing Supply and Markets
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