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...

Understanding the workings of whole economies is essential for sound policy advice - but not necessarily for accurate forecasts.  Structural models play a major role at most central banks and many other governmental agencies, yet almost none forecast the financial crisis and ensuing...

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 consider the reasons for nowcasting, how nowcasts can be achieved, and the use and timing of information.  The existence of contemporaneous data such as surveys is a major difference from forecasting, but many of the recent lessons about forecasting remain relevant.  Given the extensive...

Using an extension of general-to-specific modelling, based on the recent developments of impulse-indicator saturation (IIS), we consider selecting significant step indicators from a saturating set to capture location shifts.  The approximate non-centrality of the test is derived for a variety...

Trygve Haavelmo's Probability Approach aimed to implement economic theories, but he later recognized their incompleteness. Although he did not explicitly consider model selection, we apply it when theory-relevant variables, {xt}, are retained without selection while selecting other candidate...

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...

We model expenditure on food in the USA, using an extended time series.  Even when a theory is essentially 'correct', it can manifest serious mis-specification if just fitted to data, ignoring its observed characteristics and major external events such as wars, recessions and policy changes. ...

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...

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...