A Monte Carlo comparison of GMM and QMLE estimators for short dynamic panel data models with spatial errors

Yu Bai, Shaofu Zhou, Zhaoyuan Fan

Research output: Contribution to journalArticleResearchpeer-review

Abstract

We suggest a generalized spatial system GMM (SGMM) estimation for short dynamic panel data models with spatial errors and fixed effects when n is large and T is fixed (usually small). Monte Carlo studies are conducted to evaluate the finite sample properties with the quasi-maximum likelihood estimation (QMLE). The results show that, QMLE, with a proper approximation for initial observation, performs better than SGMM in general cases. However, it performs poorly when spatial dependence is large. QMLE and SGMM perform better for different parameters when there is unknown heteroscedasticity in the disturbances and the data are highly persistent. Both estimates are not sensitive to the treatment of initial values. Estimation of the spatial autoregressive parameter is generally biased when either the data are highly persistent or spatial dependence is large. Choices of spatial weights matrices and the sign of spatial dependence do affect the performance of the estimates, especially in the case of the heteroscedastic disturbance. We also give empirical guidelines for the model.

Original languageEnglish
Pages (from-to)376-409
Number of pages34
JournalJournal of Statistical Computation and Simulation
Volume88
Issue number2
DOIs
Publication statusPublished - 22 Jan 2018
Externally publishedYes

Keywords

  • Generalized spatial system GMM estimation
  • Monte carlo studies
  • Quasi-maximum likelihood estimation
  • Short dynamic panel data models
  • Spatial errors

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