Standard income inequality indices can be interpreted as a measure of welfare loss entailed in departures from equality of outcomes, for egalitarian social welfare functions defined on the distribution of outcomes. But such a welfare interpretation has been criticized for a long time on the...

The delta method is commonly used to calculate confidence intervals of functions of estimated parameters that are differentiable with non-zero, bounded derivatives. When the delta method is inappropriate, researchers usually first use a bootstrap procedure where they i) repeatedly take a draw...

This paper is concerned with testing the time series implications of the capital asset pricing model (CAPM) due to Sharpe (1964) and Lintner (1965), when the number of securities, N, is large relative to the time dimension, T, of the return series. In the case of cross-sectionally correlated...

When considering multiple hypothesis tests simultaneously, standard statistical techniques will lead to over-rejection of null hypotheses unless the multiplicity of the testing framework is explicitly considered. In this paper we discuss the Romano-Wolf multiple hypothesis correction, and...

Asymptotic and bootstrap tests are studied for testing whether there is a relation of stochastic dominance between two distributions. These tests have a null hypothesis of nondominance, with the advantage that, if this null is rejected, then all that is left is dominance. This also leads us to...

The paper proposes and applies statistical tests for poverty dominance that check for whether poverty comparisons can be made robustly over ranges of poverty lines and classes of poverty indices. This helps provide both normative and statistical confidence in establishing poverty rankings across...

We apply a recently proposed method to disentangle unobserved heterogeneity from risk in returns to education. We replicate the original study on US men and extend to US women, UK men and German men. Most original results are not robust. A college education cannot universally be considered an...

Forecasting errors pose a serious problem of identification, often neglected in empirical applications. Any attempt of estimating choice models under uncertainty may lead to severely biased results in the presence of forecasting errors even when individual expectations on future events are...

In this paper we describe an alternative iterative approach for the estimation of linear regression models with high-dimensional fixed-effects such as large employer-employee data sets. This approach is computationally intensive but imposes minimum memory requirements. We also show that the...

There is a large theoretical literature on methods for estimating causal effects under unconfoundedness, exogeneity, or selection-on-observables type assumptions using matching or propensity score methods. Much of this literature is highly technical and has not made inroads into empirical...