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The Out-of-Sample Forecasting Performance of Non-Linear Models of Regional Housing Prices in the US

  • Mehmet Balcilar


    (Department of Economics, Eastern Mediterranean University)

  • Rangan Gupta


    (Department of Economics, University of Pretoria)

  • Stephen M. Miller


    (Department of Economics, University of Nevada, Las Vegas)

This paper provides out-of-sample forecasts of linear and non-linear models of US and Census regions housing prices. The forecasts include the traditional point forecasts, but also include interval and density forecasts of the housing price distributions. The non-linear smooth-transition autoregressive model outperforms the linear autoregressive model in point forecasts at longer horizons, but the linear autoregressive model dominates the non-linear smooth-transition autoregressive model at short horizons. In addition, we generally do not find major differences in performance for the interval and density forecasts between the linear and non-linear models. Finally, in a dynamic 25-step ex-ante and interval forecasting design, we, once again, do not find major differences between the linear and nonlinear models.

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Paper provided by University of Nevada, Las Vegas , Department of Economics in its series Working Papers with number 1209.

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Length: 55 pages
Date of creation: Aug 2012
Date of revision:
Handle: RePEc:nlv:wpaper:1209
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