Abstract
In this paper, we study the effects of government spending with a behavioral macroeconomic model in which agents have limited cognitive capabilities and use simple heuristics to forecast output and inflation. However, thanks to a learning mechanism, agents can revise their forecasting rule according to its performance. This feature produces endogenous and self-fulfilling waves of optimistic and pessimistic beliefs (animal spirits). This framework allows us to show that the short-run spending multiplier is state-dependent. The multiplier is stronger under either extreme optimism or pessimism and reduces in periods of tranquility. Furthermore, the more the central bank focuses on output stabilization, the smaller the multiplier. We also show that periods of increasing public debt are characterized by intense pessimism, while intense optimism occurs in periods of decreasing debt. This allows us to show that governments face a trade-off between the stabilization of the animal spirits and the stabilization of public debt. Then, we show that the existence of this trade-off has implications also for the output stabilization.© 2020, Elsevier B.V. All rights reserved. The attached document (embargoed until 12/02/2023) is an author produced version of a paper published in JOURNAL OF ECONOMIC BEHAVIOR & ORGANIZATION uploaded in accordance with the publisher’s self- archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it.
Original language | English |
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Pages (from-to) | 247-263 |
Journal | Journal of Economic Behavior and Organization |
Volume | 171 |
Early online date | 12 Feb 2020 |
DOIs | |
Publication status | Published - 1 Mar 2020 |
Keywords
- Fiscal Policy
- Spending Multiplier
- Behavioral DSGE Model
- Animal Spirits
- Public Debt
- Policy State-Dependent Effects
Profiles
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Pasquale Foresti
Person: Academic