We develop a new automatically-computable test for super exogeneity, using a variant of general-to-specific modeling.  Based on the recent developments of impulse saturation applied to marginal models under the null that no impulses matter, we select the significant impulses for testing in the...

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...

We develop forecast-error taxonomies when there are unmodeled variables, forecast 'off-line'.  We establish three surprising results.  Even when an open system is correctly specified in-sample with zero intercepts, despite known future values of strongly exogenous variables, changes in...

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...

The new-Keynesian Phillips curve (NKPC) includes expected future inflation as a major feedforward variable to explain current inflation.  Models of this type are regularly estimated by replacing the expected value by the actual future outcome, then using Instrumental Variables or Generalized...

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)...

We investigate alternative robust approaches to forecasting, using a new class of robust devices, contrasted with equilibrium correction models.  Their forecasting properties are derived facing a range of likely empirical problems at the forecast origin, including measurement errors, implulses,...