Efficient estimation of panel data models with strictly exogenous explanatory variables

1999 ◽  
Vol 93 (1) ◽  
pp. 177-201 ◽  
Author(s):  
Kyung So Im ◽  
Seung C. Ahn ◽  
Peter Schmidt ◽  
Jeffrey M. Wooldridge
Keyword(s):  
Panel Data ◽  
Data Models ◽  
Efficient Estimation ◽  
Panel Data Models ◽  
Explanatory Variables

Level-Based Estimation of Dynamic Panel Models

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Gabriel Montes-Rojas ◽  
Walter Sosa-Escudero ◽  
Federico Zincenko
Keyword(s):  
Monte Carlo ◽  
Panel Data ◽  
Dynamic Panel Data ◽  
Data Models ◽  
Efficient Estimation ◽  
Panel Data Models ◽  
Dynamic Panel ◽  
Dynamic Panel Data Models ◽  
Moment Conditions ◽  
Optimal Estimator

AbstractThis paper develops an alternative estimator for linear dynamic panel data models based on parameterizing the covariances between covariates and unobserved time-invariant effects. A GMM framework is used to derive an optimal estimator based on moment conditions in levels, with no efficiency loss compared to the classic alternatives like (Arellano, M., and S. Bond. 1991. “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations.” Review of Economic Studies 58 (2): 277–297), (Ahn, S. C., and P. Schmidt. 1995. “Efficient Estimation of Models for Dynamic Panel Data.” Journal of Econometrics 68 (1): 5–27) and (Ahn, S. C., and P. Schmidt. 1997. “Efficient Estimation of Dynamic Panel Data Models: Alternative Assumptions and Simplified Estimation.” Journal of Econometrics 76: 309–321). Still, we show analytically and by Monte Carlo simulations that the new procedure leads to efficiency improvements for certain data generating processes. The framework also leads to a very simple test for unobserved effects.

scholarly journals Semiparametric efficient estimation of dynamic panel data models

2007 ◽  
Vol 136 (1) ◽  
pp. 281-301 ◽  
Author(s):  
Byeong U. Park ◽  
Robin C. Sickles ◽  
Léopold Simar
Keyword(s):  
Panel Data ◽  
Dynamic Panel Data ◽  
Data Models ◽  
Efficient Estimation ◽  
Panel Data Models ◽  
Dynamic Panel ◽  
Dynamic Panel Data Models

scholarly journals ORACLE EFFICIENT VARIABLE SELECTION IN RANDOM AND FIXED EFFECTS PANEL DATA MODELS

2012 ◽  
Vol 29 (1) ◽  
pp. 115-152 ◽  
Author(s):  
Anders Bredahl Kock
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Data Models ◽  
Orthogonality Condition ◽  
Panel Data Models ◽  
Explanatory Variables ◽  
True Model ◽  
Relevant Variables ◽  
Error Terms ◽  
As If

This paper generalizes the results for the Bridge estimator of Huang, Horowitz, and Ma (2008) to linear random and fixed effects panel data models which are allowed to grow in both dimensions. In particular, we show that the Bridge estimator isoracle efficient. It can correctly distinguish between relevant and irrelevant variables and the asymptotic distribution of the estimators of the coefficients of the relevant variables is the same as if only these had been included in the model, i.e. as if an oracle had revealed the true model prior to estimation.In the case of more explanatory variables than observations we prove that the Marginal Bridge estimator can asymptotically correctly distinguish between relevant and irrelevant explanatory variables if the error terms are Gaussian. Furthermore, a partial orthogonality condition of the same type as in Huang et al. (2008) is needed to restrict the dependence between relevant and irrelevant variables.

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