Per citar aquest document: http://ddd.uab.cat/record/102038
Two-sided learning in new keynesian models : dynamics, (lack of) convergence and the value of information
Matthes, Christian
Rondina, Francesca
Universitat Autònoma de Barcelona. Unitat de Fonaments de l'Anàlisi Econòmica
Universitat Autònoma de Barcelona. Institut d'Anàlisi Econòmica

Data: 2012
Descripció: 31 p.
Col·lecció: Working papers ; 913.12
Resum: This paper investigates the role of learning by private agents and the central bank (two-sided learning) in a New Keynesian framework in which both sides of the economy have asymmetric and imperfect knowledge about the true data generating process. We assume that all agents employ the data that they observe (which may be distinct for different sets of agents) to form beliefs about unknown aspects of the true model of the economy, use their beliefs to decide on actions, and revise these beliefs through a statistical learning algorithm as new information becomes available. We study the short-run dynamics of our model and derive its policy recommendations, particularly with respect to central bank communications. We demonstrate that two-sided learning can generate substantial increases in volatility and persistence, and alter the behavior of the variables in the model in a signifficant way. Our simulations do not converge to a symmetric rational expectations equilibrium and we highlight one source that invalidates the convergence results of Marcet and Sargent (1989). Finally, we identify a novel aspect of central bank communication in models of learning: communication can be harmful if the central bank's model is substantially mis-specified.
Drets: Tots els drets reservats
Llengua: Anglès.
Document: workingPaper
Matèria: Política monetària ; Informació, Teoria de la, en l'economia

Adreça alternativa: http://hdl.handle.net/2072/204102


31 p, 455.3 KB

El registre apareix a les col·leccions:
Documents de recerca > Working papers

 Registre creat el 2012-12-11, darrera modificació el 2016-06-11



   Favorit i Compartir