There are two margins to improve forecasting models of the U.S. recession probability: including additional variables and using a different functional form. Using out-of-sample and cross validation methods, I systematically compare the performance of various forecasting models that differ in terms of variables included and functional forms used. I find substantial gains from including additiona...

We propose a method for optimal portfolio selection using a Bayesian framework that addresses two major shortcomings of the Markowitz approach: the ability to handle higher moments and estimation error. We employ the skew normal distribution which has many attractive features for modeling multivariate returns. Our results suggest that it is important to incorporate higher order moments in portf...

Given a randomly drawn sample, calibration weighting can provide double protection against the selection bias resulting from unit nonresponse. This means that if either an assumed linear prediction model or an implied unit selection model holds, the resulting estimator will be asymptotically unbiased in some sense. The functional form of the selection model when using linear calibration adjustm...