On the Selection of Forecasting Models

Lutz Kilian and Atsushi Inoue

No 3809, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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.

Keywords: Forecast accuracy; Model selection; Structural change; Information criteria; Simulated out-of-sample method (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Date: 2003-03
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ets
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Citations: View citations in EconPapers (32)

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Related works:
Journal Article: On the selection of forecasting models (2006)
Working Paper: On the selection of forecasting models (2003)
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