Publication Type
PhD Dissertation
Version
publishedVersion
Publication Date
5-2016
Abstract
Asymptotically refined and heteroskedasticity robust inferences are considered for spatial linear and panel regression models, based on the quasi maximum likelihood (QML) or the adjusted concentrated quasi score (ACQS) approaches. Refined inferences are achieved through bias correcting the QML estimators, bias correcting the t-ratios for covariate effects, and improving tests for spatial effects; heteroskedasticity-robust inferences are achieved through adjusting the quasi score functions. Several popular spatial linear and panel regression models are considered including the linear regression models with either spatial error dependence (SED), or spatial lag dependence (SLD), or both SED and SLD (SARAR), the linear regression models with higher-order spatial effects, SARAR(p; q), and the fixed-effects panel data models with SED or SLD or both. Asymptotic properties of the new estimators and the new inferential statistics are examined. Extensive Monte Carlo experiments are run, and the results show that the proposed methodologies work really well.
Keywords
bias correction, bootstrapping, QMLE, Robust QMLE, Robust VC estimation, spatial models
Degree Awarded
PhD in Economics
Discipline
Econometrics
Supervisor(s)
YANG, Zhenlin
Publisher
Singapore Management University
City or Country
Singapore
Citation
LIU, Shew Fan.
On Refined and Robust Inferences for Spatial Econometric Models. (2016).
Available at: https://ink.library.smu.edu.sg/etd_coll/132
Copyright Owner and License
Author
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.