Dans ce travail nous explorons la contribution en terme d’introduire de l’hétérogénéité inter ménages d’intégrer une fonction de demande flexible dans un cadre d’analyse de modélisation en équilibre général calculable de type micro-simulation multi-ménages intégré...

In this paper, we provide an overview of approaches used to model income distribution and poverty in CGE models. CGE models have started to use income distribution functional forms such as the lognormal, Pareto, beta distribution and Kernel non-parametric methods to apply GFT poverty indices....

This paper proposes tests for stochastic dominance in mobility based on the empirical likelihood ratio. Two views of mobility are considered, either based on measures of absolute mobility or on transition matrices. First-order and second-order dominance conditions in mobility are first derived,...

In this paper, we explore the contribution of introducing a flexible form for household consumption in a macro-micro modelling context for poverty and income distribution analysis. The almost ideal demand system exhibits interesting features allowing for the introduction of inter-household...

This paper highlights the idea of combining CGE modeling with a micro-household model (micro-simulation) to generate a convergent solution, thus providing the basis to perform counterfactual analysis of trade and fiscal policies, and their impact on poverty. In recent years, a number of papers...

We discuss the difficult question of measuring the effects of asymmetric information problems on resource allocation. Three problems are examined: moral hazard, adverse selection, and asymmetric learning. One theoretical conclusion, drawn by many authors, is that information problems may...

In this paper, we investigate the information content of implied probabilities (Back and Brown, 1993) to improve estimation in unconditional moment conditions models. We propose and evaluate two 3-step euclidian empirical likelihood estimators and their bias-correction versions for weakly...

We propose a new nonparametric estimator for the density function of multivariate bounded data. As frequently observed in practice, the variables may be partially bounded (e.g., nonnegative) or completely bounded (e.g., in the unit interval). In addition, the variables may have a point mass. We...

In this paper we estimate density functions for positive multivariate data. We propose a semiparametric approach. The estimator combines gamma kernels or local linear kernels, also called boundary kernels, for the estimation of the marginal densities with semiparametric copulas to model the...

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