Rochina Barrachina, Maria Engracia; (2000) Panel data sample selection models. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

In this thesis estimators for "fixed-effects" panel data sample selection models are discussed, mostly from a theoretical point of view but also from an applied one. Besides the general introduction and conclusions (chapters 1 and 6, respectively) the thesis consists of four main chapters. In chapter 2 we are concerned about the finite sample performance of Wooldridge (1995) and Kyriazidou's (1997) estimators. Chapter 3 introduces a new estimator. The estimation procedure is an extension of the familiar two-steps sample selection technique to the case where one correlated selection rule in two time periods generates the sample. Some non-parametric components are introduced. We investigate the finite sample performance for the estimators in chapters 2 and 3 through Monte Carlo simulation experiments. In chapter 4 we apply the estimators in the previous chapters to estimate the return to actual labour market experience for females, using a panel of twelve years. All these estimators rely on the assumption of strict exogeneity of regressors in the equation of interest, conditional on individual specific effects and the selection mechanism. This assumption is likely to be violated in many applications. For instance, life history variables are often measured with error in survey data sets, because they contain a retrospective component. We show how non-strict exogeneity and measurement error can be taken into account within the methods. In chapter 5 we propose two semiparametric estimators under the assumption that the selection function depends on the conditional means of some observable variables. The first is a "weighted double pairwise difference estimator" because it is based in the comparison of individuals in time differences. The second is a "single pairwise difference estimator" because only differences over time for a given individual are required. We investigate the finite sample properties of these estimators by Monte Carlo experiments

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