arbiter
JournalsAccount

DSGE Model Forecasting: Rational Expectations Versus Adaptive Learning

Anders Warne

Journal of Forecasting2026https://doi.org/10.1002/for.70155article
AJG 2ABDC A
Weight
0.50

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 and 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.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1002/for.70155

Or copy a formatted citation

@article{anders2026,
  title        = {{DSGE Model Forecasting: Rational Expectations Versus Adaptive Learning}},
  author       = {Anders Warne},
  journal      = {Journal of Forecasting},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/for.70155},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

DSGE Model Forecasting: Rational Expectations Versus Adaptive Learning

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.