Statistical Inference on Semiparametric Spatial Additive Model
Ran Yan and
Journal of Mathematics Research, 2020, vol. 12, issue 2, 1
There has been a growing interest on using nonparametric and semiparametric modelling techniques for the analysis of spatial data because of their powerfulness in extracting the underlying local patterns in the data. In this study, stimulated by the Boston house price data, we apply a semiparametric spatial additive model to incorporation of spatial e ects in regression models. For this semiparametric model, we develop a linear hypothesis test of parametric coecients as well as a test for the existence of the spatial e ects. For the problem of variable selection, the adaptive Lasso method was applied. Monte Carlo simulation studies are conducted to illustrate the finite sample performance of the proposed inference procedures. Finally, an application in Boston housing data is studied.
JEL-codes: R00 Z0 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ibn:jmrjnl:v:12:y:2020:i:2:p:1
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