Extreme Conditional Quantiles for Panel Data Model with Individual Effects and Heteroscedastic Extremes

2021 ◽  
Author(s):  
Yanxi Hou ◽  
Xuan Leng ◽  
Yinggang Zhou
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Panel Data Model ◽  
Conditional Quantiles ◽  
Individual Effects

scholarly journals Estimation of a panel data model with parametric temporal variation in individual effects

2005 ◽  
Vol 126 (2) ◽  
pp. 241-267 ◽  
Author(s):  
Chirok Han ◽  
Luis Orea ◽  
Peter Schmidt
Keyword(s):  
Panel Data ◽  
Temporal Variation ◽  
Data Model ◽  
Panel Data Model ◽  
Individual Effects

Estimation of a nonlinear panel data model with semiparametric individual effects

2013 ◽  
Vol 175 (1) ◽  
pp. 46-59 ◽  
Author(s):  
Wayne-Roy Gayle ◽  
Soiliou Daw Namoro
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Panel Data Model ◽  
Individual Effects

Separating different individual effects in a panel data model

2019 ◽  
Vol 22 (2) ◽  
pp. 173-187
Author(s):  
Christine Amsler ◽  
Peter Schmidt
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Panel Data Model ◽  
Socioeconomic Background ◽  
Production Frontier ◽  
Epidemiological Analysis ◽  
Explanatory Variables ◽  
Individual Effects ◽  
Partial Correlations

Summary In this paper we consider a panel data model with individual effects that are arbitrarily correlated with the explanatory variables. The effects are composed as the sum of two different interpretable components, such as inefficiency versus heterogeneity in a production frontier setting, or ability versus socioeconomic background in an earnings function, or genetics versus environment in an epidemiological analysis. We wish to predict the two components separately. This is made possible by assuming that there are observables that are correlated with the first component but not with the second, and other observables that are correlated with the second component but not with the first. This can be true in terms of either simple correlations or partial correlations.

Vehicle Manufacturers' Decisions on Recall Timing: a Synthetic Panel Data Model

2019 ◽  
Vol 37 (3) ◽  
pp. 1-26
Author(s):  
Yong-Kyun Bae
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Panel Data Model ◽  
Synthetic Panel

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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