Predicting Downturns in the US Housing Market: A Bayesian Approach |
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Authors: | Rangan Gupta Sonali Das |
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Institution: | (1) Department of Economics, University of Pretoria, Pretoria, 0002, South Africa;(2) Logistics and Quantitative Methods, CSIR Built Environment, P.O. Box: 395, Pretoria, 0001, South Africa |
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Abstract: | This paper estimates Bayesian Vector Autoregressive (BVAR) models, both spatial and non-spatial (univariate and multivariate),
for the twenty largest states of the US economy, using quarterly data over the period 1976:Q1–1994:Q4; and then forecasts
one-to-four quarters-ahead real house price growth over the out-of-sample horizon of 1995:Q1–2006:Q4. The forecasts are evaluated
by comparing them with those from an unrestricted classical Vector Autoregressive (VAR) model and the corresponding univariate
variant of the same. Finally, the models that produce the minimum average Root Mean Square Errors (RMSEs), are used to predict
the downturns in the real house price growth over the recent period of 2007:Q1–2008:Q1. The results show that the BVARs, in
whatever form they might be, are the best performing models in 19 of the 20 states. Moreover, these models do a fair job in
predicting the downturn in 18 of the 19 states. |
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Keywords: | |
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