NONPARAMETRIC ESTIMATION OF VARYING COEFFICIENT DYNAMIC PANEL DATA MODELS

We suggest using a class of semiparametric dynamic panel data models to capture individual variations in panel data. The model assumes linearity in some continuous/discrete variables that can be exogenous/endogenous and allows for nonlinearity in other weakly exogenous variables. We propose a nonparametric generalized method of moments (NPGMM) procedure to estimate the functional coefficients, and we establish the consistency and asymptotic normality of the resulting estimators.

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Bias-corrected method of moments estimators for dynamic panel data models

Econometrics and Statistics ◽

10.1016/j.ecosta.2021.07.001 ◽

2021 ◽

Author(s):

Jörg Breitung ◽

Sebastian Kripfganz ◽

Kazuhiko Hayakawa

Keyword(s):

Panel Data ◽

Method Of Moments ◽

Dynamic Panel Data ◽

Data Models ◽

Panel Data Models ◽

Dynamic Panel ◽

Dynamic Panel Data Models ◽

Moments Estimators

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Dynamic Panel Data Models with Spatial Correlation

Jahrbücher für Nationalökonomie und Statistik ◽

10.1515/jbnst-2008-5-612 ◽

2008 ◽

Vol 228 (5-6) ◽

Author(s):

Reinhard Hujer ◽

Paulo J. M. Rodrigues ◽

Katja Wolf

Keyword(s):

Panel Data ◽

Spatial Correlation ◽

Generalized Method Of Moments ◽

Dynamic Panel Data ◽

Data Models ◽

Estimation Methods ◽

Panel Data Models ◽

Dynamic Panel ◽

Active Labour Market Policy ◽

Dynamic Panel Data Models

SummaryThis paper presents an overview of recently developed estimation methods for dynamic panel data models with spatial correlation. We discuss the specification, the main assumptions and the implications of the model. The most important estimation strategy is the application of Generalized Method of Moments (GMM). The focus lies on the derivation of the moment conditions, the estimation of the degree of spatial correlation and the specification of the optimal weighting matrix. Finally we estimate an augmented matching function to analyze the effects of active labour market policy programs in Germany using two different weighting schemes.

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THE ASYMPTOTIC PROPERTIES OF THE SYSTEM GMM ESTIMATOR IN DYNAMIC PANEL DATA MODELS WHEN BOTH N AND T ARE LARGE

Econometric Theory ◽

10.1017/s0266466614000449 ◽

2014 ◽

Vol 31 (3) ◽

pp. 647-667 ◽

Cited By ~ 28

Author(s):

Kazuhiko Hayakawa

Keyword(s):

Panel Data ◽

Asymptotic Properties ◽

Generalized Method Of Moments ◽

Dynamic Panel Data ◽

Data Models ◽

Panel Data Models ◽

Dynamic Panel ◽

Dynamic Panel Data Models ◽

System Gmm ◽

Gmm Estimator

In this paper, we derive the asymptotic properties of the system generalized method of moments (GMM) estimator in dynamic panel data models with individual and time effects when both N and T, the dimensions of cross-section and time series, are large. Specifically, we show that the two-step system GMM estimator is consistent when a suboptimal weighting matrix where off-diagonal blocks are set to zero is used. Such consistency results theoretically support the use of the system GMM estimator in large N and T contexts even though it was originally developed for large N and small T panels. Simulation results indicate that the large N and large T asymptotic results approximate the finite sample behavior reasonably well unless persistency of data is strong and/or the variance ratio of individual effects to the disturbances is large.

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Estimation of Nonlinear Dynamic Panel Data Models with Individual Effects

Mathematical Problems in Engineering ◽

10.1155/2014/672610 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 2

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.

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