Cover Image

Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection

We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantially. The states in which housing markets have been the most volatile are the states in which model change and parameter shifts have been needed the most.
Year of publication:
2015
Authors: Bork, Lasse ; Møller, Stig V.
Published in:
International Journal of Forecasting. - Elsevier, ISSN 0169-2070. - Vol. 31.2015, 1, p. 63-78
Publisher: Elsevier
Subject: Forecasting housing markets | Kalman filtering methods | Model change | Parameter shifts | Boom-bust cycle
Online Resource
Check full text access |
Check Google Scholar
In libraries world-wide (WorldCat)
In German libraries (KVK)
subito order
I need help
Type of publication: Article
Source:
Persistent link: https://www.econbiz.de/10011117246
Similar items by subject
Similar items by person