Characterization of the asymptotic distribution of semiparametric M-estimators

Asymptotic theory for M-estimators of boundaries

Asymptotic Distribution-free Tests for Semiparametric Regressions

This article proposes a new general methodology for constructing nonparametric asymptotic distribution-free tests for semiparametric hypotheses in regression models. Tests are based on the difference between the estimated restricted and unrestricted regression errors’ distributions. A suitable integral transformation of this difference renders the tests asymptotically distribution-free, with li...

On the strong consistency of asymptotic M-estimators

The aim of this article is to simplify Pfanzagl’s proof of consistency for asymptotic maximum likelihood estimators, and to extend it to more general asymptotic M -estimators. The method relies on the existence of a sort of contraction of the parameter space which admits the true parameter as a fixed point. The proofs are short and elementary.

The Asymptotic Distribution of Estimators with Overlapping Simulation Draws

In this paper we study the asymptotic distribution of simulation estimators where the same set of simulation draws are used for all observations under general conditions that do not require the function used in the simulation to be smooth. We consider two cases: estimators that solve a system of simulated moments and estimators that maximize a simulated likelihood. Many simulation estimators us...

New M-estimators in semiparametric regression with errors in variables

In the regression model with errors in variables, we observe n i.i.d. copies of (Y, Z) satisfying Y = fθ0(X) + ξ and Z = X + ε involving independent and unobserved random variables X, ξ, ε plus a regression function fθ0, known up to some finite dimensional θ. The common densities of the Xi’s and of the ξi’s are unknown whereas the distribution of ε is completely known. We aim at estimating the ...