Publication Type
Working Paper
Version
publishedVersion
Publication Date
8-2018
Abstract
In this paper, M-estimation and inference methods are developed for spatial dynamic panel data models with correlated random effects, based on short panels. The unobserved individual-specific effects are assumed to be correlated with the observed time-varying regressors linearly or in a linearizable way, giving the so-called correlated random effects model, which allows the estimation of effects of time-invariant regressors. The unbiased estimating functions are obtained by adjusting the conditional quasi-scores given the initial observations, leading to M-estimators that are consistent, asymptotically normal, and free from the initial conditions except the process starting time. By decomposing the estimating functions into sums of terms uncorrelated given idiosyncratic errors, a hybrid method is developed for consistently estimating the variance-covariance matrix of the M-estimators, which again depends only on the process starting time. Monte Carlo results demonstrate that the proposed methods perform well in finite sample.
Keywords
Adjusted quasi score, Dynamic panels, Correlated random effects, Initial-conditions, Martingale difference, Spatial effects, Short panels
Discipline
Econometrics
Research Areas
Econometrics
First Page
1
Last Page
46
Publisher
SMU Economics and Statistics Working Paper Series, No. 15-2018
City or Country
Singapore
Citation
LI, Liyao and YANG, Zhenlin.
Spatial dynamic panel data models with correlated random effects. (2018). 1-46.
Available at: https://ink.library.smu.edu.sg/soe_research/2194
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Comments
Published in Journal of Econometrics (2021) DOI: 10.1016/j.jeconom.2020.05.016