Essais en économetrie et économie de l'éducation
This these is organized in three chapter; the first two chapters are in Econometrics and the third in labor Economics.  The econometrics chapters focuses on estimating parameters in data rich environments. I investigate how to establish causal effect or treatment effect  in high dimensional setting using regularization techniques. The last chapter of this thesis focuses on the  outcomes associated with general and specific education. In particular I study the effect of specialization in high school on college major choice and performance in  college.  In the first chapter, entitled \textquotedblleft Regularized LIML for many instruments \textquotedblright (joint with Marine Carrasco), I consider the efficient estimation of a finite dimensional parameter in a linear model where the number of potential instruments is very large or infinite. The use of many moment conditions improves the asymptotic efficiency of the instrumental variables estimators. I propose regularized a versions of the limited information maximum likelihood (LIML) based on three different regularizations: Tikhonov, Landweber Fridman, and principal components.  The second chapter, entitled \textquotedblleft Efficient estimation with many weak instruments using regularization techniques \textquotedblright, (Joint with Marine Carrasco), extends  the previous works, to allow for the presence of a large number of weak instruments or weak identification.  The problem of weak instruments is due to a very small concentration parameter. To boost the concentration parameter, I propose to increase the number of instruments to a large number or even up to a continuum. I show that normalized regularized 2SLS and LIML are consistent and asymptotically normally distributed.  The third chapter of this thesis is entitled \textquotedblleft High school human capital portfolio and  college outcomes" investigates  the trade-off between acquiring specialized skills, in high school, which will be useful for a particular college major and acquiring a package of skills that diversifies risk across majors. Using the 1980 High School and Beyond (HS\&B) survey, I study the empirical relationship between college performance and different  types of courses taken during formal high school education. This panel shows that students sort into majors according to the subject in which they acquired more skills. However, I  find a U-shaped relation between diversification and college performance, suggesting a trad-off between specialization and diversification. The underlying  trade-off is assessed by estimating a structural model of high school human capital acquisition and college major choice. Policies experiments suggest that one more high school quantitative course increases enrollment in  Science Technology Engineering, and Math (STEM) majors by  4 points percentage.
High-dimensional models LIML Many weak instruments MSE Regularization Human capital Discrete Choice College major