A common approach in the design of experiment for the problem of comparing two means from a normal distribution is to assume knowledge of the ratio of the population variances. The optimal sampling ratio is proportional to the square root of this quantity. In this paper it is demonstrated that a...

A common approach in the design of experiment for the problem of comparing two means from a normal distribution is to assume knowledge of the ratio of the population variances. The optimal sampling ratio is proportional to the square root of this quantity. In this paper it is demonstrated that a...

A common approach in the design of experiment for the problem of comparing two means from a normal distribution is to assume knowledge of the ratio of the population variances. The optimal sampling ratio is proportional to the square root of this quantity. In this paper it is demonstrated that a...

We provide methods for inference on a finite dimensional parameter of interest, theta in Re^{d_theta}, in a semiparametric probability model when an infinite dimensional nuisance parameter, g, is present. We depart from the semiparametric literature in that we do not require that the pair...

This paper considers semiparametric efficient estimation of conditional moment models with possibly nonsmooth residuals in unknown parametric components (θ) and unknown functions (h) of endogenous variables. We show that: (1) the penalized sieve minimum distance(PSMD) estimator (ˆθ,ˆh) can...

Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics. In this article we develop modeling and inference tools for counterfactual distributions based on regression methods. The counterfactual scenarios that we consider...

This paper considers semiparametric efficient estimation of conditional moment models with possibly nonsmooth residuals in unknown parametric components (theta) and unknown functions (h) of endogenous variables. We show that: (1) the penalized sieve minimum distance (PSMD) estimator...

A multiplier bootstrap procedure for construction of likelihood-based confidence sets is considered for finite samples and a possible model misspecification. Theoretical results justify the bootstrap consistency for a small or moderate sample size and allow to control the impact of the parameter...

A multiplier bootstrap procedure for construction of likelihood-based confidence sets is considered for finite samples and a possible model misspecification. Theoretical results justify the bootstrap consistency for a small or moderate sample size and allow to control the impact of the parameter...