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PREDICTION‐FOCUSED MODEL SELECTION FOR AUTOREGRESSIVE MODELS
Author(s) -
Claeskens Gerda,
Croux Christophe,
Van Kerckhoven Johan
Publication year - 2007
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2007.00487.x
Subject(s) - akaike information criterion , bayesian information criterion , autoregressive model , model selection , information criteria , selection (genetic algorithm) , mathematics , series (stratigraphy) , autoregressive integrated moving average , star model , extension (predicate logic) , time series , mean squared error , bayesian probability , econometrics , statistics , computer science , machine learning , paleontology , biology , programming language
Summary In order to make predictions of future values of a time series, one needs to specify a forecasting model. A popular choice is an autoregressive time‐series model, for which the order of the model is chosen by an information criterion. We propose an extension of the focused information criterion (FIC) for model‐order selection, with emphasis on a high predictive accuracy (i.e. the mean squared forecast error is low). We obtain theoretical results and illustrate by means of a simulation study and some real data examples that the FIC is a valid alternative to the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of a prediction model. We also illustrate the possibility of using the FIC for purposes other than forecasting, and explore its use in an extended model.