نتایج جستجو برای: GMM Classification JEL: C23

تعداد نتایج: 507867  

1998
Richard Blundell Stephen Bond

Estimation of the dynamic error components model is considered using two alternative linear estimators that are designed to improve the properties of the standard firstdifferenced GMM estimator. Both estimators require restrictions on the initial conditions process. Asymptotic efficiency comparisons and Monte Carlo simulations for the simple AR(1) model demonstrate the dramatic improvement in p...

2002
Harald Badinger Werner G. Müller Gabriele Tondl

We estimate the speed of income convergence for a sample of 196 European NUTS 2 regions over the period 1985-1999. So far there is no direct estimator available for dynamic panels with strong spatial dependencies. We propose a two-step procedure, which involves first spatial filtering of the variables to remove the spatial correlation, and application of standard GMM estimators for dynamic pane...

2006
Frank Windmeijer

This chapter gives an account of the recent literature on estimating models for panel count data. Specifically, the treatment of unobserved individual heterogeneity that is correlated with the explanatory variables and the presence of explanatory variables that are not strictly exogenous are central. Moment conditions are discussed for these type of problems that enable estimation of the parame...

2005
Frank Windmeijer

Using many moment conditions can improve efficiency but makes the usual GMM inferences inaccurate. Two step GMM is biased. Generalized empirical likelihood (GEL) has smaller bias but the usual standard errors are too small in instrumental variable settings. In this paper we give a new variance estimator for GEL that addresses this problem. It is consistent under the usual asymptotics and under ...

2004
Thomas Zwick

This paper measures the productivity impact of shop-floor employee involvement. On the basis of a representative German establishment data set, the study finds that the introduction of team-work and autonomous work groups, and a reduction of hierarchies in 1996/97 significantly increased average establishment productivity in 1997 – 2000. The estimation strategy controls for unobserved invariant...

2012
Erik Meijer Laura Spierdijk Tom Wansbeek

Measurement error causes a downward bias when estimating a panel data linear regression model. The panel data context offers various opportunities to derive moment conditions that result in consistent GMM estimators. We consider three sources of moment conditions: (i) restrictions on the intertemporal covariance matrix of the errors in the equations, (ii) heteroskedasticity and nonlinearity in ...

2003
Ismael Sanz Francisco J. Velázquez Francisco Javier Velazquez Theodore Bergstrom Norman Gemmell Richard Kneller John Ashworth Carmela Martin

Following the present atmosphere of budgetary cuts we analyze the effects of fiscal consolidation on the composition of government expenditures by functions. Using a dynamic voter group decision model and exploiting the panel structure of the dataset – 26 OECD countries over the period 1970-1997by GMM estimation we find that fiscal adjustments protect social expenditure. Nevertheless, we find t...

2002
Meghan R. Busse Andrew B. Bernard

This paper derives consistent standard errors for a panel Tobit model in the presence of correlated errors. The problem is framed in the context of Newey and West (1987), considering the Tobit model as a special case of a GMM estimator. JEL codes: C23, C24

2002
William Greene

Bertschek and Lechner (1998) propose several variants of a GMM estimator based on the period specific regression functions for the panel probit model. The analysis is motivated by the complexity of maximum likelihood estimation and the possibly excessive amount of time involved in maximum simulated likelihood estimation. But, for applications of the size considered in their study, full likeliho...

2002
William Greene

Bertschek and Lechner (1998) propose several variants of a GMM estimator based on the period specific regression functions for the panel probit model. The analysis is motivated by the complexity of maximum likelihood estimation and the possibly excessive amount of time involved in maximum simulated likelihood estimation. But, for applications of the size considered in their study, full likeliho...

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