Open AccessQuasi Maximum Likelihood for MESS Varying Coefficient Panel Data Models with Fixed Effects
Author(s) -
Yan Liu
Publication year - 2021
Publication title -
journal of economic science research
Language(s) - English
Resource type - Journals
ISSN - 2630-5240
DOI - 10.30564/jesr.v4i3.3331
Subject(s) - estimator , econometrics , likelihood function , spatial econometrics , panel data , autoregressive model , exponential function , consistency (knowledge bases) , specification , logarithm , bayesian probability , function (biology) , maximum likelihood , mathematics , computer science , statistics , mathematical analysis , evolutionary biology , biology , geometry
The study of spatial econometrics has developed rapidly and has found wide applications in many different scientific fields, such as demography, epidemiology, regional economics, and psychology. With the deepening of research, some scholars find that there are some model specifications in spatial econometrics, such as spatial autoregressive (SAR) model and matrix exponential spatial specification (MESS), which cannot be nested within each other. Compared with the common SAR models, the MESS models have computational advantages because it eliminates the need for logarithmic determinant calculation in maximum likelihood estimation and Bayesian estimation. Meanwhile, MESS models have theoretical advantages. However, the theoretical research and application of MESS models have not been promoted vigorously. Therefore, the study of MESS model theory has practical significance. This paper studies the quasi maximum likelihood estimation for matrix exponential spatial specification (MESS) varying coefficient panel data models with fixed effects. It is shown that the estimators of model parameters and function coefficients satisfy the consistency and asymptotic normality to make a further supplement for the theoretical study of MESS model.