The system GMM estimator for dynamic panel data models combines moment conditions for the model in first differences with moment conditions for the model in levels. It has been shown to improve on the GMM estimator in the first differenced model in terms of bias and root mean squared error....

This discussion paper led to chapter <A href="https://books.google.nl/books?id=9TKgAgAAQBAJ&pg=PA327&lpg=PA327&dq=The+contribution+of+occupation+to+health+inequality&source=bl&ots=ARvoHIjbie&sig=HWA5I2mtJbsV_nJV5cvF6ixZmT0&hl=nl&sa=X&ei=FEj0VNbeGoHlUqjsgdgN&ved=0CDwQ6AEwAg#v=onepage&q=The%20contribution%20of%20occupation%20to%20health%20inequality&f=false">Health and Inequality</A>, pages 311-332 in: (P. Rosa Dias and O. O’Donnell (Eds)) Vol. 21 of 'Research on Economic Inequality', Emerald Group Publishing, 2013, 536 pages.<P> While it seems evident that occupations affect health, effect estimates are scarce. We...</p></a>

The system GMM estimator for dynamic panel data models combines moment conditions for the model in first differences with moment conditions for the model in levels. It has been shown to improve on the GMM estimator in the first differenced model in terms of bias and root mean squared error....

This paper introduces two easy to calculate estimators with desirable properties for the autoregressive parameter in dynamic panel data models. The estimators are (nearly) unbiased and perform satisfactorily even for small samples in either the time-series or cross-section dimension.

This paper introduces two easy to calculate estimators with desirable properties for theautoregressive parameter in dynamic panel data models. The estimators are (nearly) unbiased andperform satisfactorily even for small samples in either the time-series or cross-section dimension.