scholarly journals Finite Sample Inference for GMM Estimators in Linear Panel Data Models

2002 ◽  
Author(s):  
Stephen R. Bond ◽  
Frank Windmeijer
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Finite Sample ◽  
Finite Sample Inference

scholarly journals TESTING HOMOGENEITY IN PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS

2013 ◽  
Vol 29 (6) ◽  
pp. 1079-1135 ◽  
Author(s):  
Liangjun Su ◽  
Qihui Chen
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Data Models ◽  
Development Economic ◽  
Strong Mixing ◽  
Panel Data Models ◽  
Finite Sample ◽  
Test Statistic ◽  
Lm Test ◽  
Interactive Fixed Effects

This paper proposes a residual-based Lagrange Multiplier (LM) test for slope homogeneity in large-dimensional panel data models with interactive fixed effects. We first run the panel regression under the null to obtain the restricted residuals and then use them to construct our LM test statistic. We show that after being appropriately centered and scaled, our test statistic is asymptotically normally distributed under the null and a sequence of Pitman local alternatives. The asymptotic distributional theories are established under fairly general conditions that allow for both lagged dependent variables and conditional heteroskedasticity of unknown form by relying on the concept of conditional strong mixing. To improve the finite-sample performance of the test, we also propose a bootstrap procedure to obtain the bootstrap p-values and justify its validity. Monte Carlo simulations suggest that the test has correct size and satisfactory power. We apply our test to study the Organization for Economic Cooperation and Development economic growth model.

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.

scholarly journals A simple estimator for quantile panel data models using smoothed quantile regressions

2020 ◽  
Author(s):  
Liang Chen ◽  
Yulong Huo
Keyword(s):  
Panel Data ◽  
Bias Correction ◽  
Direct Investment ◽  
Data Models ◽  
Asymptotic Bias ◽  
Panel Data Models ◽  
Finite Sample ◽  
Quantile Regressions ◽  
Valid Inference ◽  
Finite Sample Simulations

Summary This paper considers panel data models where the idiosyncratic errors are subject to conditonal quantile restrictions. We propose a two-step estimator based on smoothed quantile regressions that is easy to implement. The asymptotic distribution of the estimator is established, and the analytical expression of its asymptotic bias is derived. Building on these results, we show how to make asymptotically valid inference on the basis of both analytical and split-panel jackknife bias corrections. Finite-sample simulations are used to support our theoretical analysis and to illustrate the importance of bias correction in quantile regressions for panel data. Finally, in an empirical application, the proposed method is used to study the growth effects of foreign direct investment.

scholarly journals Estimation of Nonlinear Dynamic Panel Data Models with Individual Effects

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yi Hu ◽  
Dongmei Guo ◽  
Ying Deng ◽  
Shouyang Wang
Keyword(s):  
Panel Data ◽  
Asymptotic Distribution ◽  
Nonlinear Dynamic ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Data Models ◽  
Panel Data Models ◽  
Finite Sample ◽  
Dynamic Panel ◽  
Dynamic Panel Data Models

This paper suggests a generalized method of moments (GMM) based estimation for dynamic panel data models with individual specific fixed effects and threshold effects simultaneously. We extend Hansen’s (Hansen, 1999) original setup to models including endogenous regressors, specifically, lagged dependent variables. To address the problem of endogeneity of these nonlinear dynamic panel data models, we prove that the orthogonality conditions proposed by Arellano and Bond (1991) are valid. The threshold and slope parameters are estimated by GMM, and asymptotic distribution of the slope parameters is derived. Finite sample performance of the estimation is investigated through Monte Carlo simulations. It shows that the threshold and slope parameter can be estimated accurately and also the finite sample distribution of slope parameters is well approximated by the asymptotic distribution.

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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