dc.contributor.author |
Gupta, Rangan
|
|
dc.contributor.author |
Kabundi, Alain
|
|
dc.contributor.author |
Miller, Stephen M.
|
|
dc.date.accessioned |
2012-05-24T11:35:36Z |
|
dc.date.available |
2012-05-24T11:35:36Z |
|
dc.date.issued |
2011 |
|
dc.description.abstract |
Several Bayesian and classical models are used to forecast house prices in 20 states in
the United States. There are two approaches: extracting common factors (principle
components) in a factor-augmented vector autoregressive or factor-augmented Bayesian
vector autoregressive models or Bayesian shrinkage in a large-scale Bayesian vector
autoregressive models. The study compares the forecast performance of the 1976:Q1 to
1994:Q4 in-sample period to the out-of-sample horizon 1995:Q1 to 2009:Q1 period. The
findings provide mixed evidence on the role of macroeconomic fundamentals in
improving the forecasting performance of time-series models. For 13 states, models that
include the information of macroeconomic fundamentals improve the forecasting
performance, while for seven states they do not. |
en |
dc.description.librarian |
nf2012 |
en |
dc.description.uri |
http://business.fullerton.edu/finance/jhr/ |
en_US |
dc.identifier.citation |
Gupta, R, Kabundi, A & Miller, SM 2011, 'Using large data sets to forecast house prices : a case study of twenty U.S. states', Journal of Housing Research, vol. 20, no. 2, pp. 161-191. |
en |
dc.identifier.issn |
1052-7001 (print) |
|
dc.identifier.uri |
http://hdl.handle.net/2263/18872 |
|
dc.language.iso |
en |
en_US |
dc.publisher |
American Real Estate Society |
en_US |
dc.rights |
American Real Estate Society |
en_US |
dc.subject |
Bayesian vector autoregressive (BVAR) model |
en |
dc.subject |
Vector autoregressive (VAR) model |
en |
dc.subject |
Factor-augmented VAR (FAVAR) model |
en |
dc.subject |
Spatial Bayesian VAR (SBVAR) model |
en |
dc.subject |
Spatial Bayesian FAVAR (SFABVAR) model |
en |
dc.subject |
Spatial large-scale BVAR (SLBVAR) model |
en |
dc.subject.lcsh |
Housing -- Prices -- United States -- Forecasting |
en |
dc.title |
Using large data sets to forecast house prices : a case study of twenty U.S. states |
en |
dc.type |
Article |
en |