In social interaction models, the identification of the network effect is based on either group size variation, structure of the network or the relative position in the network measured by the Bonacich centrality measure. These identification strategies imply the use of many instruments or...

This paper uses spatial differencing to estimate parameters in sample selection models with unobserved heterogeneity. We show that under the assumption of smooth changes across the space of unobserved site-specific heterogeneity and selection probability, key parameters of a sample selection...

In social interaction models, the identification of the network effect is based on either group size variation, structure of the network or the relative position in the network measured by the Bonacich centrality measure. These identification strategies imply the use of many instruments or...

We consider instrumental variables (IV) regression in a setting with many (possibly weak)instruments. In finite samples, the inclusion of an excessive number of moments may increasethe bias of IV estimators. We propose a Jackknife instrumental variables estimator (RJIVE) combined with...