Alternative GMM Methods for Nonlinear Panel Data Models

Breitung, Jörg; Lechner, Michael

doi:10.1017/cbo9780511625848.010

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…The ABov estimator (Arellano & Bover, 1995), for example, uses the additional moment restrictions implied by the assumption that the predetermined variable has constant correlation with the unobserved effects. In general, important recent advances have been made with regard to nonlinear GMM estimation of dynamic panel data models (see, inter alia, Ahn & Schmidt, 1995, 1997Blundell & Bond, 1998;Crepon, Kramarz & Trognon, 1998;Ahn & Schmidt, 1999;Breitung & Lechner, 1999) with regard to exploiting further orthogonality conditions. For example, Ahn & Schmidt (1995) exploit quadratic orthogonality conditions implied by the lack of serial correlation and the assumption of homoscedasticity, whereas Crepon, Kramarz & Trognon (1998) focus on the additional linear conditions implied by assumption (3.1).…”

Section: Additional Moment Restrictions Non-linear Gmm and Initial Cmentioning

confidence: 99%

A Comparative Analysis of Different IV and GMM Estimators of Dynamic Panel Data Models

Harris

Mátyás

2004

Int Statistical Rev

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It is well known that the usual procedures for estimating panel data models are inconsistent in the dynamic setting. A large number of consistent estimators however, have been proposed in the literature. This paper provides a survey of the majority of mainstream estimators, which tend to consist of IV and GMM ones. It also considers a newly proposed extension to the promising Wansbeek-Bekker estimator (Harris & Mátyás, 2000). To provide guidance to the applied researcher working on micro-datasets, the small sample performance of these estimators is evaluated using a set of Monte Carlo experiments.

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Section: Additional Moment Restrictions Non-linear Gmm and Initial Cmentioning

confidence: 99%

A Comparative Analysis of Different IV and GMM Estimators of Dynamic Panel Data Models

Harris

Mátyás

2004

Int Statistical Rev

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“…Then the unknown quantities (β, λ, f ) can be determined by a conditional moment restriction [ρ(W i t , X i t β, λ(V i ) f t )|Z i ] = 0, almost surely. In fact, the GMM estimation in Arellano & Carrasco (2003), Breitung & Lechner (1995) and Breitung & Lechner (1998) are based on similar conditional moment conditions. Let Φ k (z) be a sequence of vector of functions that can approximate any square integrable function of Z in some sense arbitrarily as k → ∞.…”

Section: Suppose Thatmentioning

confidence: 99%

GMM Estimation for High–Dimensional Panel Data Models

et al. 2022

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“…Geweke et al (1997) find that Gibbs sampling, simulated moments and maximum likelihood method using the GHK estimator, all perform reasonably well in point estimation of parameters in a three alternative 10-period probit model. Monte Carlo studies of nonlinear panel data models (Bertschek and Lechner, 1998;Breitung and Lechner, 1999) reveal that among different GMM estimators the ranking is not so obvious, while MLE performs best followed by the GMM estimator based on the optimal instruments derived from the conditional mean restrictions. Greene (2004) also find that the GMM estimator performs fairly well compared to the ML estimation.…”

Section: Nonlinear Modelsmentioning

confidence: 99%