We consider model selection for non-linear dynamic equations with more candidate variables than observations, based on a general class of non-linear-in-the-variables functions, addressing possible location shifts by impulse-indicator saturation.  After an automatic search delivers a simplified...

We consider forecasting with factors, variables and both, modeling in-sample using Autometrics so all principal components and variables can be included jointly, while tackling multiple breaks by impulse-indicator saturation.  A forecast-error taxonomy for factor models highlights the impacts...

Model selection from a general unrestricted model (GUM) can potentially confront three very different environments: over-, exact, and under-specification of the data generation process (DGP).  In the first, and most-studied setting, the DGP is nested in the GUM, and the main role of...