This paper investigates the impact of the local and the US monetary policy environments on stock returns at the different locations on the return distributions. Using data for stock returns and interest rates of 30 countries, the quantile regression technique is employed to estimate the...

Researchers and policy makers are often interested in estimating how treatments or policy interventions affect the outcomes of those most in need of help. This concern has motivated the increasingly common practice of disaggregating experimental data by groups constructed on the basis of an...

We propose inverse probability weighted estimators for the distribution functions of the potential outcomes under the unconfoundedness assumption and apply the inverse mapping to obtain the quantile functions. We show that these estimators converge weakly to zero mean Gaussian processes. A...

We investigate the impact of monetary conditions on stock market returns at different points on the return distributions. Our results reveal no association between stock returns and monetary environments at the lower quantiles. At the upper quantiles, however, we find that expansive monetary...

Dealing with endogenous regressors is a central challenge of applied research. The standard solution is to use instrumental variables that are assumed to be uncorrelated with unobservables. We instead allow the instrumental variable to be correlated with the error term, but we assume the...

Purpose – The purpose of this paper is to examine cost efficiency and its determinants in Nigerian sawmill industries. Design/methodology/approach – A stochastic frontier model is employed on a total of 160 mills randomly selected across five states in south-western Nigeria in 2006. Findings...

Los modelos de diferencias en diferencias (DD) son posiblemente la especificación econométrica más utilizada para analizar el efecto o impacto de un cambio sobre un sistema. La mayoría de sus aplicaciones se basan en datos de unidades o sujetos, por lo que puede considerarse a la...

Under the assumption of no unmeasured confounders, a large literature exists on methods that can be used to estimating average treatment effects (ATE) from observational data and that spans regression models, propensity score adjustments using stratification, weighting or regression and even the...

Estimation of average treatment effects under unconfoundedness or exogenous treatment assignment is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of...