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...

We introduce a nonparametric block bootstrap approach for Quasi-Likelihood Ratio type tests of nonlinear restrictions. Our method applies to extremum estimators, such as quasi-maximum likelihood and generalized method of moments estimators. Unlike existing parametric bootstrap procedures for...

This paper studies robustness of bootstrap inference methods under moment conditions. In particular, we compare the uniform weight and implied probability bootstraps by analyzing behaviors of the bootstrap quantiles when outliers take arbitrarily large values, and derive the breakdown points for...

We introduce a wild multiplicative bootstrap for M and GMM estimators in nonlinear models when autocorrelation structures of moment functions are unknown. The implementation of the bootstrap algorithm does not require any parametric assumptions on the data generating process. After proving its...

We study the asymptotic properties of adaptive lasso estimators when some components of the parameter of interest are strictly different than zero, while other components may be zero or may converge to zero with rate n^(-delta), with delta 0, where n denotes the sample size. First, we derive...

We derive new theoretical results on the properties of the adaptive least absolute shrinkage and selection operator (adaptive lasso) for time series regression models. In particular we investigate the question of how to conduct finite sample inference on the parameters given an adaptive lasso...

A (conservative) test is constructed to investigate the optimal lag structure for forecasting realized volatility dynamics. The testing procedure relies on the recent theoretical results that show the ability of the adaptive least absolute shrinkage and selection operator (adaptive lasso) to...

This paper studies robustness of bootstrap inference methods for instrumental variable (IV)regression models. We consider test statistics for parameter hypotheses based on the IV estimatorand generalized method of trimmed moments (GMTM) estimator introduced by Cížek (2008, 2009),and compare...

Baggerly (1998) showed that empirical likelihood is the only member in the Cressie–Read power divergence family to be Bartlett correctable. This paper strengthens Baggerly’s result by showing that in a generalized class of the power divergence family, which includes the Cressie–Read family...