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Empirical Model Discovery and Theory EvaluationAutomatic Selection Methods in Econometrics$
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David F. Hendry and Jurgen A. Doornik

Print publication date: 2014

Print ISBN-13: 9780262028356

Published to MIT Press Scholarship Online: January 2015

DOI: 10.7551/mitpress/9780262028356.001.0001

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Selecting Forecasting Models

Selecting Forecasting Models

(p.279) 23 Selecting Forecasting Models
Empirical Model Discovery and Theory Evaluation

David F. Hendry

The MIT Press

Forecasting is different: the past is fixed, but the future is not. Practical forecasting methods rely on extrapolating presently available information into the future. No matter how good such methods are, they require that the future resembles the present in the relevant attributes. Intermittent unanticipated shifts violate that requirement, and breaks have so far eluded being predicted. If no location shifts ever occurred, then the most parsimonious, congruent, undominated model in-sample would tend to dominate out of sample as well. However, if data processes are wide-sense non-stationary, different considerations matter for formulating, selecting, or using a forecasting model. In practice, the robustness to location shifts of a model formulation can be essential for avoiding systematic forecast failure, which may entail selecting from a different class of models that need not even be congruent in-sample: complete success at locating the LDGP need not improve forecasting. However, by transforming a selected congruent parsimoniously encompassing model to a more robust form before it is used in forecasting, causal information can be retained while avoiding systematic forecast failure. The chapter also notes other ways of selecting forecasting models, including model averaging and factor approaches, but focuses on transformations of selected models of the LDGP.

Keywords:   Forecasting, model averaging, factor models, unanticipated shifts, wide-sense non-stationarity, robust devices

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