Estimation of single-index model with spatial interaction
Regional Science and Urban Economics, 2017, vol. 62, issue C, 36-45
This article is concerned with the single-index model in spatial dependence data, where the spatial lag effect enters the model linearly and the relationship between variables is a nonparametric function of a linear combination of multivariate regressors. This setup avoids the so-called curse of dimensionality while still capturing important nonlinear features in high dimensional data. It also provides a convenient framework in which to model interactions between the regressors. We propose a two stage estimation strategy where the nonparametric component is established by a local linear approach and the estimation of the parametric part by GMM method, which can be seen as a direct nonlinear least squares method. We derive the asymptotic distributions of the unknowns in our model, and the procedures for constructing simultaneous confidence bands of the nonparametric function are also established. In addition, a simulation study is performed.
Keywords: Spatial dependence; Single-index setting; Reparameterization; Semiparametric GMM estimation; Asymptotic normality; Simultaneous confidence band (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:regeco:v:62:y:2017:i:c:p:36-45
Access Statistics for this article
Regional Science and Urban Economics is currently edited by D.P McMillen and Y. Zenou
More articles in Regional Science and Urban Economics from Elsevier
Series data maintained by Dana Niculescu ().