ORIGINAL ARTICLE
Fiscal multipliers, expectations and learning in a macroeconomic agent-based model
Severin Reissl,
Corresponding Author
Severin Reissl
RFF-CMCC European Institute on Economics and the Environment, Milan, Italy
Correspondence
Severin Reissl, RFF-CMCC European Institute on Economics and the Environment, Via Bergognone 34, 20144, Milan, Italy.
Email: [email protected]
Search for more papers by this authorSeverin Reissl,
Corresponding Author
Severin Reissl
RFF-CMCC European Institute on Economics and the Environment, Milan, Italy
Correspondence
Severin Reissl, RFF-CMCC European Institute on Economics and the Environment, Via Bergognone 34, 20144, Milan, Italy.
Email: [email protected]
Search for more papers by this authorFirst published: 09 April 2022
JEL classification: C63, E62, D84, E71
Managing Editor: Cathy Zhang
Abstract
This paper evaluates the government expenditure multiplier and the influence of agents' expectations and consumption choices thereupon in a pre-existing estimated macroeconomic agent-based model. If the simple consumption heuristic of the baseline model is replaced by inter-temporal optimization subject to a budget constraint based on agents' estimations of future income, the multiplier becomes significantly smaller. When agents' beliefs about the effects of expenditure shocks are explicitly introduced, they can strongly increase or decrease the multiplier. If agents are allowed learn about the effects of government expenditure on their income from repeated shocks, they are able to correctly predict these effects.
Open Research
MODEL CODE
All files necessary to reproduce the simulations shown in this paper can be downloaded from https://github.com/SReissl/CATS2
Supporting Information
| Filename | Description |
|---|---|
| ecin13084-sup-0001_DisclosureForms.pdf148.6 KB | Supplementary Material 1 |
| ecin13084-sup-0002_Appendix.pdf490.9 KB | Supplementary Material 2 |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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