TY - JOUR
T1 - Statistical Inference for Partially Linear Varying Coefficient Spatial Autoregressive Panel Data Model
AU - Feng, Sanying
AU - Tong, Tiejun
AU - Chiu, Sung Nok
N1 - This work was supported by the National Social Science Foundation of China (23BTJ061), the Humanities and Social Science Project of the Ministry of Education of China (21YJC910003), the Foundation of Henan Educational Committee (21A910004), the General Research Fund of Hong Kong (HKBU12300123, HKBU12303421), the National Natural Science Foundation of China (1207010822), and the Research Matching Grant Scheme (RMGS-2022-11-08) from the Research Grants Council of Hong Kong.
Publisher Copyright:
© 2023 by the authors.
PY - 2023/11/22
Y1 - 2023/11/22
N2 - This paper studies the estimation and inference of a partially linear varying coefficient spatial autoregressive panel data model with fixed effects. By means of the basis function approximations and the instrumental variable methods, we propose a two-stage least squares estimation procedure to estimate the unknown parametric and nonparametric components, and meanwhile study the asymptotic properties of the proposed estimators. Together with an empirical log-likelihood ratio function for the regression parameters, which follows an asymptotic chi-square distribution under some regularity conditions, we can further construct accurate confidence regions for the unknown parameters. Simulation studies show that the finite sample performance of the proposed methods are satisfactory in a wide range of settings. Lastly, when applied to the public capital data, our proposed model can also better reflect the changing characteristics of the US economy compared to the parametric panel data models.
AB - This paper studies the estimation and inference of a partially linear varying coefficient spatial autoregressive panel data model with fixed effects. By means of the basis function approximations and the instrumental variable methods, we propose a two-stage least squares estimation procedure to estimate the unknown parametric and nonparametric components, and meanwhile study the asymptotic properties of the proposed estimators. Together with an empirical log-likelihood ratio function for the regression parameters, which follows an asymptotic chi-square distribution under some regularity conditions, we can further construct accurate confidence regions for the unknown parameters. Simulation studies show that the finite sample performance of the proposed methods are satisfactory in a wide range of settings. Lastly, when applied to the public capital data, our proposed model can also better reflect the changing characteristics of the US economy compared to the parametric panel data models.
KW - empirical likelihood
KW - instrumental variable
KW - panel data
KW - partially linear varying coefficient model
KW - spatial autoregressive model
KW - two-stage least squares
KW - coefficient model
UR - http://www.scopus.com/inward/record.url?scp=85177667380&partnerID=8YFLogxK
U2 - 10.3390/math11224606
DO - 10.3390/math11224606
M3 - Journal article
AN - SCOPUS:85177667380
SN - 2227-7390
VL - 11
JO - Mathematics
JF - Mathematics
IS - 22
M1 - 4606
ER -