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On the selection of forecasting models

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Author Info
Lutz Kilian () (University of Cambridge - Faculty of Economics and Politics at Ann Arbor - Department of Economics, 611 Tappan Street, Ann Arbor , MI 48109-1220, United States.)
Atsushi Inoue () (North Carolina State University - Department of Agricultural & Resource Economics, Box 8109, 3332 Nelson Hall, Raleigh , NC 27695-8109, United States.)

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Abstract

It is standard in applied work to select forecasting models by ranking candidate models by their 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 minimize 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 overparameterized 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. JEL Classification: C22; C52; C53.

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Paper provided by European Central Bank in its series Working Paper Series with number 214.

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Length: 60 pages
Date of creation: Feb 2003
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Handle: RePEc:ecb:ecbwps:20030214

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Related research
Keywords: Model selection; forecast accuracy; structural change; information criteria; simulated out-of-sample method.;

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