.png){kind=icon}
.png){kind=icon}
Return
A nonparametric spatial regression model using partitioning estimators
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ecosta.2023.02.003.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Conventional spatial regression models are extended by modelling the spatial effects of the exogenous regressor model (SLX) as a functional coefficient. This coefficient is estimated by partitioning the domain of the spatial variable into a set of disjoint intervals and approximating the function using local Taylor expansions. The asymptotic properties of the proposed partitioning estimator are derived, and pointwise and uniform tests for the presence of spatial effects are developed. An empirical application of this work is used to study environmental Engel curves and provides strong evidence of neighbouring effects in the relationship between households' income and the amount of pollution embodied in the goods and services they consume. (c) 2023 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.
Keywords:
Spatial regression
partitioning estimator
interaction matrix
asymptotic theory
environmental Engel curve