This paper addresses the issue of outliers in finance-growth literature and provides a robust sensitivity analysis of some past studies and an updated data set. We employ the robust regression methods of median quantile regression and least trimmed squares. It shows that the findings of past...

This work describes a versatile and readily-deployable sensitivity analysis of an ordinary least squares (OLS) inference with respect to possible endogeneity in the explanatory variables of the usual k-variate linear multiple regression model. This sensitivity analysis is based on a derivation...

We propose a framework for estimation and inference about the parameters of an economic model and predictions based on it, when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We derive formulas to...

Composite indicators are increasingly important in country comparisons and in policy making. At the same time, the robustness of the results obtained and in particular of the rankings and the conclusions obtained from the analysis it is usually accepted with doubts. In this sense our proposal is...

We propose a framework for estimation and inference when the model may be misspecified. We rely on a local asymptotic approach where the degree of misspecification is indexed by the sample size. We construct estimators whose mean squared error is minimax in a neighborhood of the reference model,...