Department of Economics and Business Economics

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

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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.
Original languageEnglish
JournalInternational Journal of Forecasting
Volume31
Issue1
Pages (from-to)63-78
Number of pages16
ISSN0169-2070
DOIs
Publication statusPublished - 2015

    Research areas

  • Forecasting housing markets, Kalman filtering methods, Model change, Parameter shifts, Boom-bust cycle

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