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

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Author Info

  • Mehmet Balcilar

    (Eastern Mediterranean University)

  • Rangan Gupta

    (University of Pretoria)

  • Stephen M. Miller

    (University of Nevada, Las Vegas and University of Connecticut)

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.

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Bibliographic Info

Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2012-12.

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Length: 56 pages
Date of creation: Aug 2012
Date of revision:
Handle: RePEc:uct:uconnp:2012-12

Note: Stephen M. Miller is corresponding author
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Web page: http://www.econ.uconn.edu/
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Keywords: Forecasting; Linear and non-linear models; US and Census housing price indexes;

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Cited by:
  1. Pejman Bahramian & Mehmet Balcilar & Rangan Gupta & Patrick T. kanda, 2014. "Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation," Working Papers 201416, University of Pretoria, Department of Economics.

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