Economics at your fingertips  

Estimation of a semiparametric varying-coefficient mixed regressive spatial autoregressive model

Yanqing Sun, Yuanqing Zhang and Jianhua Z. Huang

Econometrics and Statistics, 2019, vol. 9, issue C, 140-155

Abstract: A semiparametric varying-coefficient mixed regressive spatial autoregressive model is used to study covariate effects on spatially dependent responses, where the effects of some covariates are allowed to vary with other variables. A semiparametric series-based least squares estimating procedure is proposed with the introduction of instrumental variables and series approximations of the conditional expectations. The estimators for both the nonparametric and parametric components of the model are shown to be consistent and their asymptotic distributions are derived. The proposed estimators perform well in simulations. The proposed method is applied to analyze a data set on teen pregnancy to investigate effects of neighborhood as well as other social and economic factors on the teen pregnancy rate.

Keywords: Asymptotic theory; Semiparametric varying coefficient; Series approximation; Spatial mixed regression; Teen pregnancy analysis; Two-stage least squares estimation (search for similar items in EconPapers)
Date: 2019
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. Contains open access articles

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Econometrics and Statistics is currently edited by E.J. Kontoghiorghes, H. Van Dijk and A.M. Colubi

More articles in Econometrics and Statistics from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2019-01-12
Handle: RePEc:eee:ecosta:v:9:y:2019:i:c:p:140-155