Optimal monetary policy studies typically rely on a single structural model and identification of model-specific rules that minimize the unconditional volatilities of inflation and real activity. In our proposed approach, we take a large set of structural models and look for the model-robust...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

We propose Hybrid Shrink (HS), a novel model for combining forecasts that merges forecast selection, weighting, and shrinkage concepts by shrinking forecasters’ weights to average weights, i.e., toward subset selection, or to zero. HS learns the forecast shrinkage direction by minimizing the...

Stochastic mortality models seek to forecast future mortality rates; thus, it is apparent that the objective variable should be the mortality rate expressed in the original scale. However, the performance of stochastic mortality models-in terms, that is, of their goodness-of-fit and prediction...

Many structural econometric models include latent variables on whose probability distributions one may wish to place minimal restrictions. Leading examples in panel data models are individual-specific variables sometimes treated as "fixed effects" and, in dynamic models, initial conditions. This...