Most studies on the determinants of poverty do not consider that relative importance each these can vary depending degree suffered by group poor people. For Mexico’s case, carried out so far contemplate this approach, even though there is wide variation in among different groups poor. Investigating differences important to design better policies for fighting poverty, which how variable explains...

We propose a new family of error distributions for model-based quantile regression, which is constructed through a structured mixture of normal distributions. The construction enables fixing specific percentiles of the distribution while, at the same time, allowing for varying mode, skewness and tail behavior. It thus overcomes the severe limitation of the asymmetric Laplace distribution – the ...

There is general agreement that skill-enhancing school reforms in the Middle East and North Africa (MENA) region are necessary for economic, political and social reasons. Using studentlevel data from Jordan and Tunisia, this study assesses the relationship between skills and the following school incentive and accountability measures: pedagogical autonomy, school competition, freedom to hire and...

Quantile regression has important applications in risk management, portfolio optimization, and asset pricing. The current paper studies estimation, inference and …nancial applications of quantile regression with cointegrated time series. In addition, a new cointegration model with varying coe¢ cients is proposed. In the proposed model, the value of cointegrating coe¢ cients may be a¤ected by th...

In several regression applications, a different structural relationship might be anticipated for the higher or lower responses than the average responses. In such cases, quantile regression analysis can uncover important features that would likely be overlooked by mean regression. We develop two distinct Bayesian approaches to fully nonparametric model-based quantile regression. The first appro...

Quantile regression is a statistical technique intended to estimate, and conduct inference about the conditional quantile functions. Just as the classical linear regression methods estimate models for conditional mean function, quantile regression offers a mechanism for estimating models for conditional median function, and the full range of other conditional quantile functions. In this paper d...

To date the literature on quantile regression and least absolute deviation regression has assumed either explicitly or implicitly that the conditional quantile regression model is correctly specified. When the model is misspecified, confidence intervals and hypothesis tests based on the conventional covariance matrix are invalid. Although misspecification is a generic phenomenon and correct spe...