Theory and methods of panel data models with interactive effects
By Jushan Bai, Kunpeng Li. 2014
77 citations recorded by the source corpus.
This paper considers the maximum likelihood estimation of panel data models with interactive effects. Motivated by applications in economics and other social sciences, a notable feature of the model is that the explanatory variables are correlated with the unobserved effects. The usual within-group estimator is inconsistent. Existing methods for consistent estimation are either designed for panel data with short time periods or are less efficient. The maximum likelihood estimator has desirable properties and is easy to implement, as illustrated by the Monte Carlo simulations. This paper develops the inferential theory for the maximum likelihood estimator, including consistency, rate of convergence and the limiting distributions. We further extend the model to include time-invariant regressors and common regressors (cross-section invariant). The regression coefficients for the time-invariant regressors are time-varying, and the coefficients for the common regressors are cross-sectionally varying.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/4f2541740bd5e700c118c32bcb4065790f64d864
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Vasilis Sarafidis
- Takashi Yamagata
- Guowei Cui
- Milda Norkute
- Brantley Liddle
- M. Eberhardt
- F. Teal
- Perry Sadorsky
Citations in the corpus
- Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects (2020, methodology): “Estimation of this model has been studied by Pesaran (2006), Bai and Li (2014), Westerlund and Urbain (2015), Juodis and Sarafidis (2020), Cui et al.”