Dynamic sparse adaptive learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985998%3A_____%2F25%3A00636128" target="_blank" >RIV/67985998:_____/25:00636128 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.cerge-ei.cz/pdf/wp/Wp797.pdf" target="_blank" >https://www.cerge-ei.cz/pdf/wp/Wp797.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Dynamic sparse adaptive learning

Popis výsledku v původním jazyce

This paper studies convergence properties, including local and global strong E-stability, of the rational expectations equilibrium (REE) under non-smooth learning dynamics, and the role of monetary policy in agents’ expectation formation. In a New Keynesian model, we consider two types of informational constraints that operate jointly - Sparse Rationality under Adaptive Learning. We study the dynamics of the learning algorithm for the positive costs of attention, initialized from the equilibrium with mis-specified beliefs. We find that, for any initial beliefs, the agents’ forecasting rule converges either to the Minimum State Variable (MSV) REE, or, for large attention costs, to a rule with anchored inflation expectations. With stricter monetary policy the convergence is faster. A mis-specified forecasting rule that uses a variable not present in the MSV REE does not survive this learning algorithm. We apply the theory of non-smooth differential equations to study the dynamics of our learning algorithm.

Název v anglickém jazyce

Dynamic sparse adaptive learning

Popis výsledku anglicky

This paper studies convergence properties, including local and global strong E-stability, of the rational expectations equilibrium (REE) under non-smooth learning dynamics, and the role of monetary policy in agents’ expectation formation. In a New Keynesian model, we consider two types of informational constraints that operate jointly - Sparse Rationality under Adaptive Learning. We study the dynamics of the learning algorithm for the positive costs of attention, initialized from the equilibrium with mis-specified beliefs. We find that, for any initial beliefs, the agents’ forecasting rule converges either to the Minimum State Variable (MSV) REE, or, for large attention costs, to a rule with anchored inflation expectations. With stricter monetary policy the convergence is faster. A mis-specified forecasting rule that uses a variable not present in the MSV REE does not survive this learning algorithm. We apply the theory of non-smooth differential equations to study the dynamics of our learning algorithm.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů