The least-absolute-deviations (LAD) estimator for a median- regression model does not satisfy the standard conditions for obtaining asymptotic refinements through use of the bootstrap because the LAD objective function is not smooth. This paper overcomes this problem by smoothing the objective...

This paper studies seemingly unrelated linear models with integrated regressors and stationary errors.

It is well known that the usual techniques for estimating random and fixed effects panel data models are inconsistent in the dynamic setting. As a consequence, numerous consistent estimators have been proposed in the literature. However, all such estimators rely on certain well defined...

Brief summaries and user instruction are presented for the programs TRAMO ("Time Series regression with ARIMA Noise, Missing Observations and Outlers") and SEATS ("Signal Extraction in ARIMA Time Series").

When stochastic errors are added to data from a distribution with a sharp boundary, such as a changepoint or a frontier, nonparametric estimation of the boundary can be interpreted as a problem of deconvolution. We argue that, rather than attempting to estimate the distribution of the...

This paper proposes to use a semi parametric regression method, named Sliced Inverse Regression (SIR hereafter), to analyse ambulatory blood pressure monitoring data.

This paper presents a new simulated maximum-likelihood method that rests on estimating the likelihood nonparametrically on a simulated sample. We prove that this method, which can be used on very general models, is consistent and asymptotically efficient.

We propose a minimax statistical framework adapted to nonparametric estimation in deterministic dynamical system, which highlights the importance of the recurrence property of the observed process. Estimators are evaluated on the basis of a weighted uniform norm. The weight function rn depends...