Heterogeneous Expectations and Uncertain Inflation Target
| Autor: | Guido Traficante, Stefano Marzioni |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
050208 finance
Information set Inflation targeting 05 social sciences Economics Econometrics and Finance (miscellaneous) Stability (learning theory) Kalman filter Computer Science Applications Shock (economics) 0502 economics and business Economics Econometrics New Keynesian economics Fraction (mathematics) Adaptive learning 050207 economics |
| Zdroj: | Computational Economics. 56:601-621 |
| ISSN: | 1572-9974 0927-7099 |
| DOI: | 10.1007/s10614-019-09959-y |
| Popis: | We analyze a new Keynesian economy populated by adaptive-learning agents with heterogeneous beliefs about the time-varying inflation target. A fraction of agents is assumed to have a full and updated information set including the permanent and temporary component of the inflation target at the current period, while the remainder of agents receives a signal and use it to estimate the target components solving a Kalman filter problem. The proportion of the two strategies is endogenous and depends on a measure of past performance of predictors. We conduct stochastic simulations to assess whether different hypotheses about the information regime may affect macroeconomic stability in the short and in the long run. We find that a smaller proportion of agents using costly information is associated to larger expected losses. Nevertheless, the fraction of agents following this strategy drops signficantly in the aftermath of a shock to the inflation target because the Kalman signal extraction procedure allows to follow more closely the actual dynamics of the economy. |
| Databáze: | OpenAIRE |
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