This project supports my ongoing research in high-dimensional econometric models, with a particular focus on models featuring heterogeneous effects. The current research develops and applies advanced econometric methods, including clustering algorithms and multiple hypothesis testing procedures, to detect, characterize, and quantify heterogeneity in treatment effects across individuals, groups, and outcomes. The theoretical contributions of the project are complemented by an extensive set of simulation studies. These simulations are designed both to validate theoretical predictions and to investigate settings that fall outside the scope of existing theoretical results. Particular emphasis is placed on assessing the finite-sample performance of newly developed methods, as the underlying theory is largely based on asymptotic approximations. The simulation results provide important insights into the statistical properties of the proposed procedures, including hypothesis tests, point estimators, and confidence sets. These analyses are essential for evaluating the practical applicability and reliability of the methods in realistic empirical settings.