Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2019.1650657
The role of housing sentiment in forecasting U.S. home sales growth: evidence from a Bayesian compressed vector autoregressive model
Rangan Gupta
; Department of Economics, University of Pretoria, Pretoria, South Africa
Chi Keung Marco Lau
; Huddersfield Business School, University of Huddersfield, Huddersfield, United Kingdom
Vasilios Plakandaras
; Department of Economics, Democritus University of Thrace, Komotini, Greece
Wing-Keung Wong
; Department of Finance, Fintech Center, and Big Data Research Center, Asia University, Taichung, Taiwan; e Department of Medical Research, China Medical University Hospital, Taiwan; f Department of Economics and Finance, Hang Seng Management College, Hong
Sažetak
Accurate forecasts of home sales can provide valuable information for not only policymakers, but also financial institutions and real estate professionals. Against this backdrop, the objective of our article is to analyse the role of consumers’home buying attitudes in forecasting quarterly U.S. home sales growth. Our results show that the home sentiment index in standard classical and Minnesota prior-based Bayesian V.A.R.s fail to add to the forecasting accuracy of the growth of home sales derived from standard economic variables already included in the models. However, when shrinkage is achieved by compressing the data using a Bayesian compressed V.A.R. (instead of the parameters as in the B.V.A.R.), growth of U.S. home sales can be forecasted more accurately, with the housing market sentiment improving the accuracy of the forecasts relative to the information contained in economic variables only.
Ključne riječi
home sales; housing sentiment; classical and Bayesian vector autoregressive models
Hrčak ID:
229558
URI
Datum izdavanja:
22.1.2019.
Posjeta: 988 *