Abstract:
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.
Keywords:Housing prices; Forecasting; Factor Augmented Models; Large-Scale BVAR models (search for similar items in EconPapers) JEL-codes:C32R31 (search for similar items in EconPapers) New Economics Papers: this item is included in nep-ure Date: 2009 Note: We acknowledge the assistance of D. Liu and D. W. Jansen, who provided the data on the 308 macroeconomic indicators, as well as for clarifying all the data related issues. View list of references