We consider the semiparametric regression X t +(Z) where and (r and function, and where the variables (X, Z) are endogeneous. We propose necessary and sufficient conditions for the identification of the parameters in the presence of instrumental variables. We also focus on the estimation of . An...

We consider the general issue of estimating a nonparametric function x from the inverse problem r = Tx given estimates of the function r and of the linear transform T. Two typical examples include the estimation of a probability density function fromdata contaminated by a noise whose...

In this paper, we study the behavior of second order pseudo-maximum likelihood estimators un- der conditional variance misspecification. We first determine sufficient and essentially necessary conditions for such an estimator to be, regardless of the conditional variance (mis)specification,...

The nonparametric estimation of a regression function x from conditional moment restrictions involving instrumental variables is considered. The rate of convergence of penalized estimators is studied in the case where x is not identified from the conditional moment restriction. We also study the...

In this paper, we study the behaviour of second order pseudo-maximum likelihood estimators under conditional variance misspecification. We first determine sufficient and essentially necessary conditions for such an estimator to be, regardless of the conditional variance (mis)specification,...

By using a semiparametric specification, we examine the impact of urban concentration in economic growth on different groups of countries that we classify according to a geographical criterion or according to their level of development. Facing a significant proportion of missing data, we handle...

We consider the nonparametric regression model with an additive error that is correlated with the explanatory variables. We suppose the existence of instrumental variables that are considered in this model for the identification and the estimation of the regression function. The nonparametric...

I consider multivariate (vector) time series models in which the error covariance matrix may be time-varying. I derive a test of constancy of the error covariance matrix against the alternative that the covariance matrix changes over time. I design a new family of Lagrange-multiplier tests...

We propose a practical and flexible solution to introduce skewness in multivariate symmetrical distributions. Applying this procedure to the multivariate Student density leads to a "multivariate skew-Student" density, for which each marginal has a different asymmetry coefficient. Similarly, when...

Using the 1990-1992 wave of the French Labour Force Survey this paper analyzes the effects of different factors on the probability of leaving unemployment of French young people. It also studies duration dependence of the hazard rate while controlling for unobserved heterogeneity separately for...