Nonparametric Regression with Spatially Dependent Data
39 Pages Posted: 2 Oct 2009 Last revised: 1 May 2012
Date Written: March 30, 2010
Abstract
In this paper we present a new procedure for nonparametric regression in case of spatially dependent data. In particular, we extend usual local linear regression (along the lines of Martins-Filho and Yao, 2009) and propose a two-step method where information on spatial dependence is incorporated in the error covariance matrix, estimated nonparametrically. The finite sample performance of our proposed procedure is then shown via Monte Carlo simulations for various data generating processes.
Keywords: nonparametric smoothing, spatial dependence
JEL Classification: C14, C21
Suggested Citation: Suggested Citation
Do you have a job opening that you would like to promote on SSRN?
Recommended Papers
-
"Location, Location, Location!" the Market for Vacant Urban Land: New York 1835-1900
By Jeremy Atack and Robert A. Margo
-
Where is the Economics in Spatial Econometrics?
By Luisa Corrado and Bernard Fingleton
-
Correlation Testing in Time Series, Spatial and Cross-Sectional Data
-
Mostly Pointless Spatial Econometrics?
By Stephen Gibbons and Henry G. Overman
-
The Future of Spatial Econometrics
By Joris Pinkse and Margaret E. Slade
-
Estimation and Hypothesis Testing for Nonparametric Hedonic House Price Functions
-
Tiebout Dynamics: Neighborhood Response to a Central-City/Suburban House-Price Differential
By Paul Thorsnes and John W. Reifel