In this paper we examine the consequences of a decrease in exits of older workers on youth employment, allowing for job-to-job áows. We distinguish between young workers with no experience and experienced workers and we consider jobs with di§erent experience requirements. Using a theoretical...

We estimate the informal-formal sector pay gap throughout the conditional wage distribution using panel data from Brazil, Mexico and South Africa. We control for time-invariant unobservables and identification is stemming from inter-sector movers. We control for observables in a non-linear way...

A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations...

Socially responsible firms are expected by European regulators to create shared value for their shareholders, their stakeholders and society. Yet how to generate profits while providing public good is still academically debated. This paper argues that corporate social responsibility encompasses...

A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations...

We propose a new method of estimation in high-dimensional linear regression model. It allows for very weak distributional assumptions including heteroscedasticity, and does not require the knowledge of the variance of random errors. The method is based on linear programming only, so that its...

A large number of non linear conditional heteroskedastic models have been proposed in the literature and practitioners do not have always the tools to choose the correct specification. In this article, our main interest is to know if usual choice criteria lead them to choose the good...

In this paper, we compare two different variable selection approaches for linear regression models: Autometrics (automatic general-to-specific selection) and LASSO (ℓ1-norm regularization). In a simulation study, we show the performance of the methods considering the predictive power (forecast...

In this paper, we present a new methodology based on vine copulas to estimate multivariate distributions in high dimensions, taking advantage of the diversity of vine copulas. Considering the huge number of vine copulas in dimension n, we introduce an efficient selection algorithm to build and...

In this paper, we present a new methodology based on vine copulas to estimate multivariate distributions in high dimensions, taking advantage of the diversity of vine copulas. Considering the huge number of vine copulas in dimension n, we introduce an efficient selection algorithm to build and...