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.

scholarly journals Testing for Serial Correlation in Linear Panel-data Models

2003 ◽  
Vol 3 (2) ◽  
pp. 168-177 ◽  
Author(s):  
David M. Drukker
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Fixed Effects ◽  
Serial Correlation ◽  
Error Term ◽  
Data Models ◽  
Panel Data Model ◽  
Standard Errors ◽  
Panel Data Models ◽  
General Conditions

Because serial correlation in linear panel-data models biases the standard errors and causes the results to be less efficient, researchers need to identify serial correlation in the idiosyncratic error term in a panel-data model. A new test for serial correlation in random- or fixed-effects one-way models derived by Wooldridge (2002) is attractive because it can be applied under general conditions and is easy to implement. This paper presents simulation evidence that the new Wooldridge test has good size and power properties in reasonably sized samples.

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.

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.

Semiparametric estimation of generalized transformation panel data models with nonstationary error

2020 ◽  
Vol 23 (3) ◽  
pp. 386-402
Author(s):  
Xi Wang ◽  
Songnian Chen
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Bilateral Trade ◽  
Marginal Distribution ◽  
Semiparametric Estimation ◽  
Estimation Method ◽  
Data Models ◽  
Panel Data Models ◽  
Finite Sample ◽  
Foreign Countries

Summary Early studies of the generalized transformation panel data model resorted to the identical marginal distribution of the error term over time. This stationarity condition is restrictive for many applications, especially as the number of time periods increases. This paper considers nonstationary censored generalized transformation panel data models where the idiosyncratic error has an unknown nonseparable form and admits a flexible relationship between the observable and the unobservable. We propose an estimation method, and establish the consistency and asymptotic normality for the proposed estimator. Simulation results illustrate the good performance of our estimator in a finite sample. We apply the proposed method to bilateral trade issues of the U.S.A. and foreign countries.

ROBUST PARAMETER ESTIMATION FOR RANDOM EFFECT PANEL DATA MODEL IN THE PRESENCE OF HETEROSCEDASTICITY AND INFLUENTIAL OBSERVATIONS

2021 ◽  
Vol 4 (4) ◽  
pp. 561-569
Author(s):  
Sani Muhammad ◽  
Suleiman Shamsuddeen ◽  
Ismail G. Baoku
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Random Effect ◽  
Estimation Method ◽  
Panel Data Model ◽  
Influential Observations ◽  
Weighting Method ◽  
Robust Parameter ◽  
Robust Parameter Estimation ◽  
Combine Problem

Panel data estimators can strongly be biased and inconsistent in the presence of heteroscedasticity and anomalous observations called influential observations (IOs) in Random effect (RE) panel data model. The existing methods (LWS, WLSF, WLSDRGP) address only the problem of IO but fail to remedy the combine problem of heteroscedasticity and IOs.  Therefore, in this research we develop a method that will remedy the combine problem of heteroscedasticity and IOs based on robust heteroscedasticity consistent covariance matrix (RHCCM) estimator and fast improvised influential distance (FIID) weighting method denoted by WLSFIID. The simulation and numerical evidences show that our proposed estimation method is more efficient than the existing methods by providing smallest bias, and smallest standard error of HC4 and HC5.

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