Stig Vinther Møller

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

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection. / Bork, Lasse; Møller, Stig Vinther.

I: International Journal of Forecasting, Bind 31, Nr. 1, 2015, s. 63-78.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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Bork, Lasse ; Møller, Stig Vinther. / Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection. I: International Journal of Forecasting. 2015 ; Bind 31, Nr. 1. s. 63-78.

Bibtex

@article{c4bdac9813124e4083534e1c28244198,
title = "Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection",
abstract = "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.",
keywords = "Forecasting housing markets, Kalman filtering methods, Model change, Parameter shifts, Boom-bust cycle",
author = "Lasse Bork and M{\o}ller, {Stig Vinther}",
year = "2015",
doi = "10.1016/j.ijforecast.2014.05.005",
language = "English",
volume = "31",
pages = "63--78",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier BV",
number = "1",

}

RIS

TY - JOUR

T1 - Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection

AU - Bork, Lasse

AU - Møller, Stig Vinther

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Forecasting housing markets

KW - Kalman filtering methods

KW - Model change

KW - Parameter shifts

KW - Boom-bust cycle

U2 - 10.1016/j.ijforecast.2014.05.005

DO - 10.1016/j.ijforecast.2014.05.005

M3 - Journal article

VL - 31

SP - 63

EP - 78

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 1

ER -