factor models

We propose a method to estimate time invariant cyclical DSGE models using the information provided by a variety of filtering approaches. We treat data filtered with alternative procedures as contaminated proxy of the relevant model-based quantities and estimate structural and non-structural parameters jointly using an unobservable component structure.

We explore whether forecasting an aggregate variable using information on its disaggregate components can improve the prediction mean squared error over first forecasting the disaggregates and then aggregating those forecasts, or, alternatively, over using only lagged aggregate information in forecasting the aggregate.

This paper explores the usefulness of bagging methods in forecasting economic time series from linear multiple regression models. We focus on the widely studied question of whether the inclusion of indicators of real economic activity lowers the prediction mean-squared error of forecast models of US consumer price inflation.