The aim of the paper is to relax distributional assumptions on the error terms, often imposed in parametric sample selection models to estimate causal effects, when plausible exclusion restrictions are not available. Within the principal stratification framework, we approximate the true...

In clusterwise regression analysis, the goal is to predict a response variable based on a set of explanatory variables, each with cluster-specific effects. Nowadays, the number of candidates is typically large: whereas some of these variables might be useful, some others might contribute very...

The paper analyzes the relationship between stock prices and fundamentals for a large sample of US stocks in the last ten years using a random coefficient model. Heterogeneity and omitted variable bias are properly taken into account with model coefficients being allowed to vary across time and...

The synthetic control method (SCM) allows estimation of the causal effect of an intervention in settings where panel data on just a few treated units and control units are available. We show that the existing SCM as well as its extensions can be easily modified to estimate how much of the total...

In this paper we show that the testable implications derived in Huber and Mellace (2013) are the best possible to detect invalid instruments, in the presence of heterogeneous treatment effects and endogeneity. We also provide a formal proof of the fact that those testable implications are only...

In heterogeneous treatment effect models with endogeneity, identification of the LATE typically relies on the availability of an exogenous instrument monotonically related to treatment participation. We demonstrate that a strictly weaker local monotonicity condition identifies the LATEs on...