This paper investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyse both asymptotic approximations and bootstrap methods for...

A large literature studies the predictability of stock returns by other lagged nancialvariables in a predictive regression setting. A common feature of widely used testingprocedures is a failing robustness, which may lead to misleading conclusions determinedby the particular features of a small...

We characterize the robustness of subsampling procedures by deriving a general formula for the breakdown point of subsampling quantiles. This breakdown point can be very low for moderate subsampling block sizes, which implies the fragility of subsampling procedures, even if they are applied to...

We study the robustness of block resampling procedures for time series. We first derive a setof formulas to quantify their quantile breakdown point. For the block bootstrap and the sub-sampling, we find a very low quantile breakdown point. A similar robustness problem arisesin relation to...

We study the robustness of block resampling procedures for time series. We first derive a set of formulas to characterize their quantile breakdown point. For the moving block bootstrap and the subsampling, we find a very low quantile breakdown point. A similar robustness problem arises in...

We characterize the robustness of subsampling procedures by deriving a formula for the breakdown point of subsampling quantiles. This breakdown point can be very low for moderate subsampling block sizes, which implies the fragility of subsampling procedures, even if they are applied to robust...