Nonparametric regression with spatially dependent data

This file is part of IDEAS, which uses RePEc data

Nonparametric regression with spatially dependent data

Author Info

Stefano Magrini () (Department of Economics, University Of Venice Cà Foscari)
Margherita Gerolimetto () (University Of Venice Cà Foscari)

Additional information is available for the following registered author(s):

Stefano Magrini

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.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

Publisher Info
Paper provided by University of Venice "Ca' Foscari", Department of Economics in its series Working Papers with number 2009_20.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length: 32
Date of creation: 2009
Date of revision:
Handle: RePEc:ven:wpaper:2009_20

Contact details of provider:
Postal: Cannaregio, S. Giobbe no 873 , 30121 Venezia
Phone: 2574183
Fax: 2574176
Email:
Web page: http://www.dse.unive.it
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: (Renato Dalla Venezia).

Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

This paper has been announced in the following NEP Reports:

NEP-ALL-2009-10-10 (All new papers)
NEP-ECM-2009-10-10 (Econometrics)
NEP-GEO-2009-10-10 (Economic Geography)
NEP-URE-2009-10-10 (Urban & Real Estate Economics)

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

Martins-Filho, Carlos & Yao, Feng, 2009. "Nonparametric regression estimation with general parametric error covariance," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 309-333, March. [Downloadable!] (restricted)
Naisyin Wang, 2003. "Marginal nonparametric kernel regression accounting for within-subject correlation," Biometrika, Oxford University Press for Biometrika Trust, vol. 90(1), pages 43-52, March.
Xiao Z. & Linton O.B. & Carroll R.J. & Mammen E., 2003. "More Efficient Local Polynomial Estimation in Nonparametric Regression With Autocorrelated Errors," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 980-992, January. [Downloadable!] (restricted)

Full references

Statistics
Access and download statistics

Did you know? IDEAS also indexes book chapters.

This page was last updated on 2009-11-25.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.