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...

We consider selecting an econometric model when there is uncertainty over both the choice of variables and the occurrence and timing of multiple location shifts.  The theory of general-to-simple (Gets) selection is outlined and its efficacy demonstrated in a new set of simulation experiments...

We evaluate automatically selecting the relevant variables in an econometric model from a large candidate set.  General-to-specific selection is outlined for a constant model in orthogonal variables, where only one decision is required to select, irrespective of the number of regressors (N T)...

Unpredictability arises from intrinsic stochastic variation, unexpected instances of outliers, and unanticipated extrinsic shifts of distributions.  We analyze their properties, relationships, and different effects on the three arenas in the title, which suggests considering three associated...

This paper describes some recent advances and contributions to our understanding of economic forecasting. The framework we develop helps explain the findings of forecasting competitions and the prevalence of forecast failure. It constitutes a general theoretical background against which recent...

Disputes about econometric methodology partly reflect a lack of evidence on alternative approaches. We reconsider econometric model selection from a computer-automation perspective, focusing on general-to-specific reductions, embodied in PcGets. Starting from a general congruent model, standard...

Our strategy for automatic selection in potentially non-linear processes is: test for non-linearity in the unrestricted linear formulation; if that test rejects, specify a general model using polynomials, to be simplified to a minimal congruent representation; finally select by encompassing...

Although a general unrestricted model may under-specify the data generation process, especially when breaks occur, model selection can still improve over estimating a prior specification.  Impulse-indicator saturation (IIS) can 'correct' non-constant intercepts induced by location shifts in...