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Forecasting house prices using dynamic model averaging
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Resumo(s)
This work project applies the Dynamic Model Averaging methodology to forecast
quarterly house price growth in Portugal, Spain, Italy, Ireland, the Euro Area and the United
States. This recent econometric technique uses the Kalman filter to recursively estimate
dynamic models and ultimately produces a forecast by averaging these models using a
prediction performance criterion. Results show the superior predictive ability of this
methodology when compared to the usual autoregressive benchmarks. Furthermore, we make
use of the model’s outputs to provide a comparative analysis of the six series, concluding that
there is no single predictor transversally important for all series.
Descrição
Palavras-chave
House prices Dynamic model averaging Kalman filter Forecasting