scholarly journals The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models

2007 ◽  
Author(s):  
Wladimir Raymond ◽  
Pierre Mohnen ◽  
Franz C. Palm ◽  
Sybrand Schim van der Loeff
Keyword(s):  
Maximum Likelihood ◽  
Panel Data ◽  
Maximum Likelihood Estimator ◽  
Sample Selection ◽  
Dynamic Panel Data ◽  
Likelihood Estimator ◽  
Dynamic Panel ◽  
Selection Models

THE INFORMATION BOUND OF A DYNAMIC PANEL LOGIT MODEL WITH FIXED EFFECTS

2001 ◽  
Vol 17 (5) ◽  
pp. 913-932 ◽  
Author(s):  
Jinyong Hahn
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Fixed Effects ◽  
Dynamic Panel Data ◽  
Consistent Estimator ◽  
Likelihood Estimator ◽  
Dynamic Panel ◽  
Conditional Maximum Likelihood ◽  
Information Bound ◽  
Conditional Maximum

In this paper, I calculate the semiparametric information bound in two dynamic panel data logit models with individual specific effects. In such a model without any other regressors, it is well known that the conditional maximum likelihood estimator yields a √n-consistent estimator. In the case where the model includes strictly exogenous continuous regressors, Honoré and Kyriazidou (2000, Econometrica 68, 839–874) suggest a consistent estimator whose rate of convergence is slower than √n. Information bounds calculated in this paper suggest that the conditional maximum likelihood estimator is not efficient for models without any other regressor and that √n-consistent estimation is infeasible in more general models.

scholarly journals Quasi–maximum Likelihood Estimation of Linear Dynamic Short-T panel-data Models

2016 ◽  
Vol 16 (4) ◽  
pp. 1013-1038 ◽  
Author(s):  
Sebastian Kripfganz
Keyword(s):  
Maximum Likelihood ◽  
Panel Data ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Dynamic Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Dynamic Panel ◽  
Linear Dynamic ◽  
Quasi Maximum Likelihood

In this article, I describe the xtdpdqml command for the quasi–maximum likelihood estimation of linear dynamic panel-data models when the time horizon is short and the number of cross-sectional units is large. Based on the theoretical groundwork by Bhargava and Sargan (1983, Econometrica 51: 1635–1659) and Hsiao, Pesaran, and Tahmiscioglu (2002, Journal of Econometrics 109: 107–150), the marginal distribution of the initial observations is modeled as a function of the observed variables to circumvent a short- T dynamic panel-data bias. Both random-effects and fixed-effects versions are available.

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