The present paper addresses the selection-of-regressors issue into a general discrimination framework. We show how this framework is useful in unifying various procedures for selecting regressors and helpful in understanding the different strategies underlying these procedures. We review...

We consider a principal–agent model of environmental regulation with adverse selection, where firms are regulated through contracts. We show how the model allows to recover information on structural cost parameters. We use a semiparametric method to estimate consistently such parameters...

We propose a general procedure for testing that a regression function has a prescribed parametric form. We allow for multivariate regressors, non-normal errors and heteroscedasticity of unknown form. The test relies upon a nonparametric linear estimation method, such as a sieves expansion or the...

We consider testing the significance of a subset of covariates in a nonparamet- ric regression. These covariates can be continuous and/or discrete. We propose a new kernel-based test that smoothes only over the covariates appearing under the null hypothesis, so that the curse of dimensionality...

I propose a new theoretical framework to assess the approximate validity of overidentifying moment restrictions. Their approximate validity is evaluated by the divergence between the true probability measure and the closest measure that imposes the moment restrictions of interest. The divergence...

In empirical research, one commonly aims to obtain evidence in favor of restrictions on parameters, appearing as an economic hypothesis, a consequence of economic theory, or an econometric modeling assumption. I propose a new theoretical framework based on the Kullback–Leibler information to...

We address the issue of lack-of-fit testing for a parametric quantile regression. We propose a simple test that involves one-dimensional kernel smoothing, so that the rate at which it detects local alternatives is independent of the number of covariates. The test has asymptotically gaussian...

We consider conditional moment models under semi-strong identification. Identification strength is directly defined through the conditional moments that flatten as the sample size increases. Our new minimum distance estimator is consistent, asymptotically normal, robust to semi-strong...

[eng] Losses of Welfare and Market Power in the French Food Industry by Pascal Lavergne, Vincent Réquillart and Michel Simioni . We study the practical problems of estimating losses of welfare resulting from market power. Particular attention is paid to the specification of demand functions,...