High-Dimensional Methods in Microeconometrics

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In this project I propose several advancements for the implementation of high-dimensional methods in microeconometrics. The aim of this project is to be derive useful theoretical-, as well as simulation results for applied micro-econometricians interested in these methods. Specifically, building on previous work, I plan to advance the implementation of the sparse-group lasso for multiple outcome correction in several crucial directions: First, I plan to examine previously suggested methods for tuning parameter selection for their usefulness in data driven-tuning parameter selection. I can show that cross-validation leads to misleading results concerning the structure of the true signal and we therefore need to look for other methods. Second, I plan to examine a different path: choosing the tuning parameter based on policy objectives and/or economic theory. Third, I want to compare other multiple testing procedures and recent (high-dimensional) advances in multiple testing corrections with the sparse-group lasso method in a well-designed and empirically motivated simulation study to find out which of these methods is the most suitable in which empirical research set-up.