Consistency in Estimation and Model Selection of Dynamic Panel Data Models with Fixed Effects

Econometrics

Guangjie Li¹ 

[1] Cardiff Business School, Cardiff University, Aberconway Building, Colum Drive, Cardiff CF10 3EU, UK; E-Mail

关键词: dynamic panel data model with fixed effects;    incidental parameter problem;    consistency in estimation;    model selection;    bayesian model averaging;   

DOI  :  10.3390/econometrics3030494

来源: mdpi

【 摘 要 】

We examine the relationship between consistent parameter estimation and model selection for autoregressive panel data models with fixed effects. We find that the transformation of fixed effects proposed by Lancaster (2002) does not necessarily lead to consistent estimation of common parameters when some true exogenous regressors are excluded. We propose a data dependent way to specify the prior of the autoregressive coefficient and argue for comparing different model specifications before parameter estimation. Model selection properties of Bayes factors and Bayesian information criterion (BIC) are investigated. When model uncertainty is substantial, we recommend the use of Bayesian Model Averaging to obtain point estimators with lower root mean squared errors (RMSE). We also study the implications of different levels of inclusion probabilities by simulations.

【 授权许可】

CC BY   
© 2015 by the author; licensee MDPI, Basel, Switzerland.