Different Estimators for Stochastic Parameter Panel Data Models with Serially Correlated Errors

2018 ◽  
Vol 7 (3) ◽  
pp. 423-434 ◽  
Author(s):  
Mohamed Reda Abonazel
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Correlated Errors ◽  
Stochastic Parameter

scholarly journals Long-Run Effects in Large Heterogenous Panel Data Models with Cross-Sectionally Correlated Errors

2015 ◽  
Vol 2015 (223) ◽  
Author(s):  
Alexander Chudik ◽  
◽  
Kamiar Mohaddes ◽  
M. Hashem Pesaran ◽  
Mehdi Raissi ◽  
...  
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Correlated Errors ◽  
Long Run

NONLINEAR PANEL DATA MODELS WITH DISTRIBUTION-FREE CORRELATED RANDOM EFFECTS

2021 ◽  
pp. 1-25
Author(s):  
Yu-Chin Hsu ◽  
Ji-Liang Shiu
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Conditional Distribution ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Data Models ◽  
Panel Data Model ◽  
Panel Data Models ◽  
Likelihood Functions ◽  
Correlated Random Effects

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a semiparametric family of average likelihood functions of observables by combining the conditional distribution of the model and the recovered distribution of the unobserved heterogeneity, and show that the parameters in the nonlinear panel data model and in the CRE specification are identifiable. Based on the identification result, we propose a sieve maximum likelihood estimator. Compared with the conventional parametric CRE approaches, the advantage of our method is that it is not subject to misspecification on the distribution of the CRE. Furthermore, we show that the average partial effects are identifiable and extend our results to dynamic nonlinear panel data models.

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