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Discussion Paper Details

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Title: On the Selection of Forecasting Models

Author(s): Atsushi Inoue and Lutz Kilian

Publication Date: March 2003

Keyword(s): forecast accuracy, information criteria, model selection, simulated out-of-sample method and structural change

Programme Area(s): International Macroeconomics

Abstract: It is standard in applied work to select forecasting models by ranking candidate models by their prediction mean squared error (PMSE) in simulated out-of-sample (SOOS) forecasts. Alternatively, forecast models may be selected using information criteria (IC). We compare the asymptotic and finite-sample properties of these methods in terms of their ability to mimimize the true out-of-sample PMSE, allowing for possible misspecification of the forecast models under consideration. We first study a covariance stationary environment. We show that under suitable conditions the IC method will be consistent for the best approximating model among the candidate models. In contrast, under standard assumptions the SOOS method will select over-parameterized models with positive probability, resulting in excessive finite-sample PMSEs. We also show that in the presence of unmodelled structural change both methods will be inadmissible in the sense that they may select a model with strictly higher PMSE than the best approximating model among the candidate models.

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Bibliographic Reference

Inoue, A and Kilian, L. 2003. 'On the Selection of Forecasting Models'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=3809