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

Mehmet Balcilar, Rangan Gupta and Stephen Miller

No 2012-12, Working papers from University of Connecticut, Department of Economics

Abstract: 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.

Keywords: Forecasting; Linear and non-linear models; US and Census housing price indexes (search for similar items in EconPapers)
JEL-codes: C32 R31 (search for similar items in EconPapers)
Pages: 56 pages
Date: 2012-08
New Economics Papers: this item is included in nep-for and nep-ure
Note: Stephen M. Miller is corresponding author
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US (2015)
Working Paper: The Out-of-Sample Forecasting Performance of Non-Linear Models of Regional Housing Prices in the US (2012)
Working Paper: The Out-of-Sample Forecasting Performance of Non-Linear Models of Regional Housing Prices in the US (2012)
Working Paper: The Out-of-Sample Forecasting Performance of Non-Linear Models of Regional Housing Prices in the US (2012)
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