This paper considers the specification and performance of jackknife estimators of the autoregressive coefficient in a model with a near-unit root. The limit distributions of sub-sample estimators that are used in the construction of the jackknife estimator are derived, and the joint moment...

We consider robust testing on the regression parameter of a partially linear regression model, where missing responses are allowed. We derive the asymptotic behavior of the proposed test statistic under the null and contiguous alternatives. A numerical study is performed.

In this paper, we introduce a family of robust statistics which allow to decide between a parametric model and a semiparametric one. More precisely, under a generalized partially linear model, i.e., when the observations satisfy yi|(xi,ti)∼F(⋅,μi) with μi=H(η(ti)+xit β) and H a known...

Robust nonparametric equivariant M-estimators for the regression function have been extensively studied when the covariates are in Rk. In this paper, we derive strong uniform convergence rates for kernel-based robust equivariant M-regression estimator when the covariates are functional.

In this paper I consider a model for the heterogeneity and dynamics of the conditional mean and the conditional variance of standarized individual wages. In particular, I propose a dynamic panel data model with individual effects both in the mean and in a conditional ARCH type variance function....

We discuss a modified objective function strategy to obtain estimators without bias to order 1/T in nonlinear dynamic panel models with multiple effects. Estimation proceeds from a bias corrected objective function relative to some target infeasible criterion. We consider a determinant based...

This paper is concerned with the application of jackknife methods as a means of bias reduction in the estimation of autoregressive models with a unit root. It is shown that the usual jackknife estimator based on non-overlapping sub-samples does not remove fully the first-order bias as intended,...