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An in-sample and out-of-sample empirical investigation of the nonlinearity in house prices of South Africa

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  • Balcilar, Mehmet
  • Gupta, Rangan
  • Shah, Zahra B.

Abstract

This paper tests whether housing prices in the five segments of the South African housing market, namely large-middle, medium-middle, small-middle, luxury and affordable, exhibit non-linearity based on smooth transition autoregressive (STAR) models estimated using quarterly data from 1970:Q2 to 2009:Q3. Findings point to an overwhelming evidence of non-linearity in these five segments based on in-sample evaluation of the linear and non-linear models. We next provide further support for non-linearity by comparing one- to four-quarters-ahead out-of-sample forecasts of the non-linear time series model with those of the classical and Bayesian versions of the linear autoregressive (AR) models for each of these segments, for the out-of-sample horizon 2001:Q1 to 2009:Q3, using the in-sample period 1970:Q2 to 2000:Q4. Our results indicate that barring the one-, two and four-step(s)-ahead forecasts of the small segment, the non-linear model always outperforms the linear models. In addition, given the existence of strong causal relationship amongst the house prices of the five segments, the multivariate versions of the linear (classical and Bayesian) and STAR (MSTAR) models were also estimated. The MSTAR always outperformed the best performing univariate and multivariate linear models. Thus, our results highlight the importance of accounting for non-linearity, as well as the possible interrelationship amongst the variables under consideration, especially for forecasting.

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Bibliographic Info

Article provided by Elsevier in its journal Economic Modelling.

Volume (Year): 28 (2011)
Issue (Month): 3 (May)
Pages: 891-899

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Handle: RePEc:eee:ecmode:v:28:y:2011:i:3:p:891-899

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Web page: http://www.elsevier.com/locate/inca/30411

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Keywords: Bayesian autoregressive models Housing market Smooth transition autoregressive models Forecast accuracy;

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Citations

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Cited by:
  1. Mehmet Balcilar & Rangan Gupta & Stephen M. Miller, 2012. "The Out-of-Sample Forecasting Performance of Non-Linear Models of Regional Housing Prices in the US," Working papers 2012-12, University of Connecticut, Department of Economics.
  2. Novella Maugeri, 2010. "Money Illusion and Rational Expectations: New Evidence from Well Known Survey Data," Department of Economics University of Siena 606, Department of Economics, University of Siena.
  3. Tsangyao Chang & Wen-Chi Liu & Goodness C. Aye & Rangan Gupta, 2013. "Are there Housing Bubbles in South Africa? Evidence from SPSM-Based Panel KSS Test with a Fourier Function," Working Papers 201377, University of Pretoria, Department of Economics.
  4. Luis A. Gil-Alana & Goodness C. Aye & Rangan Gupta, 2012. "Testing for Persistence with Breaks and Outliers in South African House Prices," Working Papers 201233, University of Pretoria, Department of Economics.
  5. Sibel Cengiz & Afsin Sahin, 2014. "Modelling nonlinear behavior of labor force participation rate by STAR: An application for Turkey," International Journal of Economic Sciences and Applied Research (IJESAR), Technological Educational Institute (TEI) of Kavala, Greece, vol. 7(1), pages 113-127, April.
  6. Tsangyao Chang & Tsung-pao Wu & Rangan Gupta, 2013. "Are House Prices in South Africa Really Non-Stationary? Evidence from SPSM-Based Panel KSS Test with a Fourier Function," Working Papers 201324, University of Pretoria, Department of Economics.
  7. Mirriam Chitalu Chama-Chiliba & Rangan Gupta & Nonophile Nkambule & Naomi Tlotlego, 2011. "Forecasting Key Macroeconomic Variables of the South African Economy Using Bayesian Variable Selection," Working Papers 201132, University of Pretoria, Department of Economics.
  8. Roula Inglesi-Lotz & Rangan Gupta, 2011. "Relationship between House Prices and Inflation in South Africa: An ARDL Approach," Working Papers 201130, University of Pretoria, Department of Economics.

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