Quantile regression provides sports economists with a powerful research tool. Unlike least squares, it is not tied to restrictive assumptions about the distribution of the error term, which makes it particularly valuable in settings with highly skewed distributions, like sports labor markets. It...

Many social scientists believe that dumping long lists of explanatory variables into linear regression, probit, logit, and other statistical equations will successfully “control†for the effects of auxiliary factors. Encouraged by convenient software and ever more powerful computing,...

The recent financial crisis has raised numerous questions about the accuracy of value-at-risk (VaR) as a tool to quantify extreme losses. In this paper we develop data-driven VaR approaches that are based on the principle of optimal combination and that provide robust and precise VaR forecasts...