Using a Fourier polynomial expansion to generate a spatial predictor
Marius Thériault and
François Des Rosiers
International Journal of Housing Markets and Analysis, 2012, vol. 5, issue 2, 177-195
Purpose - Spatial autocorrelation in regression residuals is a major issue for the modeller because it disturbs parameter estimates and invalidates the reliability of conclusions drawn from models. The purpose of this paper is to develop an approach which generates new spatial predictors that can be mapped and qualitatively analysed while controlling for spatial autocorrelation among residuals. Design/methodology/approach - This paper explores an alternate approach using a Fourier polynomial function based on geographical coordinates to construct an additional spatial predictor that allows to capture the latent spatial pattern hidden among residuals. An empirical validation based on hedonic modelling of sale prices variation using a large dataset of house transactions is provided. Findings - Results show that the spatial autocorrelation problem is under control as shown by low Moran's Originality/value - The originality of this paper relies on the development of a new model that allows considering, simultaneously spatial and time dimension while measuring the marginal impact of environmental amenities on house prices avoiding competition with the weight matrix needed in most spatial econometric models.
Keywords: Regression analysis; Mathematics; Polynomials; Fourier polynomial expansion method; Geo-statistical approach; Hedonic price model; Spatial autocorrelation; Environmental amenities; Spatio-temporal analysis (search for similar items in EconPapers)
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