DSGE model forecasting: rational expectations vs. adaptive learning

Anders Warne

No 2768, Working Paper Series from European Central Bank

Abstract: This paper compares within-sample and out-of-sample fit of a DSGE model with rational expectations to a model with adaptive learning. The Galí, Smets and Wouters model is the chosen laboratory using quarterly real-time euro area data vintages, covering 2001Q1–2019Q4. The adaptive learning model obtains better within-sample fit for all vintages used for estimation in the forecast exercise and for the full sample. However, the rational expectations model typically predicts real GDP growth better as well as jointly with inflation. For the marginal inflation forecasts, the same holds for the inner quarters of the forecast horizon, while the adaptive learning model predicts better for the outer quarters. JEL Classification: C11, C32, C52, C53, E37

Keywords: Bayesian inference; CRPS; euro area; forecast comparison/evaluation; log score; realtime data (search for similar items in EconPapers)
Date: 2023-01
New Economics Papers: this item is included in nep-dge
Note: 563011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20232768

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