scholarly journals Semiparametric Bayesian inference for dynamic Tobit panel data models with unobserved heterogeneity

2008 ◽  
Vol 23 (6) ◽  
pp. 699-728 ◽  
Author(s):  
Tong Li ◽  
Xiaoyong Zheng
Keyword(s):  
Bayesian Inference ◽  
Panel Data ◽  
Unobserved Heterogeneity ◽  
Data Models ◽  
Panel Data Models ◽  
Semiparametric Bayesian Inference

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.

Partial effects in non-linear panel data models with correlated random effects

2021 ◽  
Author(s):  
Jason Abrevaya ◽  
Yu-Chin Hsu
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Unobserved Heterogeneity ◽  
Data Models ◽  
Panel Data Models ◽  
New Concepts ◽  
Correlated Random Effects ◽  
Non Linear ◽  
Partial Effects

Summary Nonlinearity and heterogeneity are known to cause difficulties in estimating and interpreting partial effects. This paper provides a systematic characterization of the various partial effects in nonlinear panel data models that might be of interest to empirical researchers. The interpretation of the partial effects depends upon (i) whether the distribution of unobserved heterogeneity is treated as fixed or allowed to vary with covariates, and (ii) whether one is interested in particular covariate values or an average over such values. The characterization covers partial-effects concepts already in the literature but also includes new concepts for partial effects. A simple panel probit design highlights that the different partial effects can be quantitatively very different.

scholarly journals Panel data models with nonadditive unobserved heterogeneity: Estimation and inference

10.3982/qe75 ◽  
2013 ◽  
Vol 4 (3) ◽  
pp. 453-481 ◽  
Author(s):  
Iván Fernández-Val ◽  
Joonhwah Lee
Keyword(s):  
Panel Data ◽  
Unobserved Heterogeneity ◽  
Data Models ◽  
Panel Data Models

scholarly journals Bayesian inference for random coefficient dynamic panel data models

2016 ◽  
Vol 44 (9) ◽  
pp. 1543-1559 ◽  
Author(s):  
Fang Liu ◽  
Peng Zhang ◽  
Ibrahim Erkan ◽  
Dylan S. Small
Keyword(s):  
Bayesian Inference ◽  
Panel Data ◽  
Dynamic Panel Data ◽  
Data Models ◽  
Random Coefficient ◽  
Panel Data Models ◽  
Dynamic Panel ◽  
Dynamic Panel Data Models

scholarly journals Heterogeneity, transient and persistent technical efficiency of Polish crop farms

2019 ◽  
Vol 17 (1) ◽  
pp. e0106
Author(s):  
Andrzej Pisulewski ◽  
Jerzy Marzec
Keyword(s):  
Panel Data ◽  
Technical Efficiency ◽  
New Technologies ◽  
Unobserved Heterogeneity ◽  
Stochastic Frontier ◽  
Data Models ◽  
Panel Data Models ◽  
Random Effects Model ◽  
Managerial Skills ◽  
Long Run

Accounting for heterogeneity in the measurement of farm efficiency is crucial to avoid biases related to climate and soil quality diversity in a given area. Therefore, this paper investigates the level of technical efficiency (TE) of Polish crop farms based on several stochastic frontier panel data models with different approaches to the measurement of unobserved heterogeneity, short- and long- run inefficiency. In our study, we show that ignoring farm heterogeneity can lead to underestimation of the level of TE in conventional stochastic frontier panel data models. Moreover, we have found empirically that not accounting for heterogeneity in the Generalized True Random Effects model may lead to incorrect estimates of persistent TE. The obtained results for Polish crop farms indicate that the level of transient TE (0.81) is lower than the level of persistent TE (0.88). This result suggests that Polish farms may have, for example, problems with adopting new technologies and poor managerial skills.

The Robustness of Conditional Logit for Binary Response Panel Data Models with Serial Correlation

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Do Won Kwak ◽  
Robert S. Martin ◽  
Jeffrey M. Wooldridge
Keyword(s):  
Panel Data ◽  
Unobserved Heterogeneity ◽  
Data Models ◽  
Monte Carlo Experiment ◽  
Parameter Estimates ◽  
Panel Data Models ◽  
Time Dimension ◽  
Conditional Logit ◽  
Serial Independence ◽  
Correlated Random Effects

Abstract We examine the conditional logit estimator for binary panel data models with unobserved heterogeneity. A key assumption used to derive the conditional logit estimator is conditional serial independence (CI), which is problematic when the underlying innovations are serially correlated. A Monte Carlo experiment suggests that the conditional logit estimator is not robust to violation of the CI assumption. We find that higher persistence and smaller time dimension both increase the magnitude of the bias in slope parameter estimates. We also compare conditional logit to unconditional logit, bias corrected unconditional logit, and pooled correlated random effects logit.

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