Simple Models and Biased Forecasts
Pooya Molavi
Papers from arXiv.org
Abstract:
This paper proposes a framework in which agents are constrained to use simple models to forecast economic variables and characterizes the resulting biases. It considers agents who can only entertain state-space models with no more than d states, where d measures the intertemporal complexity of a model. Agents are boundedly rational in that they can only consider models that are too simple to capture the true process, yet they use the best model among those considered. Using simple models adds persistence to forward-looking decisions and increases the comovement among them. This mechanism narrows the gap between business-cycle theory and data. In a new neoclassical synthesis model, the assumption that agents use simple models fits the data much better than the rational-expectations hypothesis. Moreover, simple models simultaneously resolve the Barro-King and forward guidance puzzles while improving the propagation of TFP shocks.
Date: 2022-02, Revised 2024-10
New Economics Papers: this item is included in nep-ban, nep-dge and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://arxiv.org/pdf/2202.06921 Latest version (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2202.06921
Access Statistics for this paper
More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().