C3 - Multiple or Simultaneous Equation Models; Multiple VariablesReturn

Modeling policy effects in the context of high-dimensional data requires a balanced consideration of omitted interaction bias and overfitting problems. This paper investigates the role of machine learning algorithms in stabilizing estimates and demonstrates the possible regularization bias caused by common LASSO methods. To overcome the three problems simultaneously, post-double selection is used to screen for the interaction terms that need to be included in the model, and the variance estimates are expanded to measure the uncertainty of the interaction effects and marginal effects. Monte Carlo simulations analyze the main factors affecting conditional and non-linear relationships: covariance and sample size. The results of empirical examples show that different model settings and estimation methods can lead to observable differences in the conclusion of treatment effect heterogeneity, and in general, post-double selection has better performance than other estimation methods.

This paper investigates dependence structures on selected world stock markets. Firstly, a non-parametric univariate measure of a persistence concerning capital markets efficiency is derived and computed. Secondly, we focus on computing of a non-parametric multivariate measure of the persistence indicating an ability of the price mechanisms to hold capital market efficiency under interaction of shocks.