State dependence in work-related training participation among British employees: A comparison of different random effects probit estimators
AbstractThis paper compares three different estimation approaches for the random effects dynamic panel data model, under the probit assumption on the distribution of the errors. These three approaches are attributed to Heckman (1981), Wooldridge (2005) and Orme (2001). The results are then compared with those obtained from generalised method of moments (GMM) estimators of a dynamic linear probability model, namely the Arellano and Bond (1991) and Blundell and Bond (1998) estimators. A model of work-related training participation for British employees is estimated using individual level data covering the period 1991-1997 from the British Household Panel Survey. This evaluation adds to the existing body of empirical evidence on the performance of these estimators using real data, which supplements the conclusions from simulation studies. The results suggest that for the dynamic random effects probit model the performance of no one estimator is superior to the others. GMM estimation of a dynamic LPM of training participation suggests that the random effects estimators are not sensitive to the distributional assumptions of the unobserved effect.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 14261.
Date of creation: Jul 2008
Date of revision: Mar 2009
state dependence; training; dynamic panel data models;
Find related papers by JEL classification:
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-04-05 (All new papers)
- NEP-ECM-2009-04-05 (Econometrics)
- NEP-LAB-2009-04-05 (Labour Economics)
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