Ypma, JY; (2013) Dynamic models of continuous and discrete outcomes; methods and applications. Doctoral thesis , UCL (University College London).

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Abstract

This thesis contains three chapters on dynamic models with discrete and continuous outcomes. In the rest chapter, I focus on indirect inference estimation. Indirect inference is used to estimate parameters in models where evaluation of the objective function directly is complicated or infeasible. Indirect inference is typically formulated as an optimization problem nesting one or more other optimization problems. In some cases the solution to the inner optimization problems can be obtained in one step, but when such a solution is not available, indirect inference estimation is computationally demanding. I show how constrained optimization methods can be used to replace the nesting of optimization problems and I provide Monte Carlo evidence showing when this approach is bene cial. The second chapter uses panel data from the United Kingdom to estimate a model of wage dynamics with labour participation where the variance in wages is decomposed in a permanent and a transitory component. Most studies that estimate similar models ignore non-participation; individuals without a wage are simply removed from the analysis. This leads to biased estimates of the parameters if working individuals are di erent in their unobservable characteristics compared to people that do not work. I use a dynamic selection model to include a discrete labour participation choice in a simple model of wage dynamics and compare the results to a version of the model that does not include labour participation. In the third chapter, I show how some of the assumptions on the dynamics of the unobservables in the second chapter can be relaxed. High dimensional integrals have to be approximated to estimate the less restrictive models. I use sparse grids and simulation methods to approximate these integrals and compare their performance on simulated data.

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