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Description
  • In this paper we evaluate the empirical relevance of learning by private agents in an estimated medium–scale DSGE model. We replace the standard rational expectation assumption in the Smets and Wouters (2007) model by a constant gain learning mechanism. If agents know the correct structure of the model and only learn about the parameters, both expectation mechanisms result in a similar fit, and only the transition dynamics that are generated by specific initial beliefs are responsible for the differences between the two approaches. If, in addition, agents use only a reduced information set in forming the perceived law of motion, the implied model dynamics change and for some initial beliefs the marginal likelihood of the model is further improved.
  • In this paper we evaluate the empirical relevance of learning by private agents in an estimated medium–scale DSGE model. We replace the standard rational expectation assumption in the Smets and Wouters (2007) model by a constant gain learning mechanism. If agents know the correct structure of the model and only learn about the parameters, both expectation mechanisms result in a similar fit, and only the transition dynamics that are generated by specific initial beliefs are responsible for the differences between the two approaches. If, in addition, agents use only a reduced information set in forming the perceived law of motion, the implied model dynamics change and for some initial beliefs the marginal likelihood of the model is further improved. (en)
Title
  • Learning in an estimated medium-scale DSGE model
  • Learning in an estimated medium-scale DSGE model (en)
skos:prefLabel
  • Learning in an estimated medium-scale DSGE model
  • Learning in an estimated medium-scale DSGE model (en)
skos:notation
  • RIV/00216208:11640/09:00334167!RIV10-MSM-11640___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(LC542), Z(MSM0021620846)
http://linked.open...iv/cisloPeriodika
  • 396
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 323395
http://linked.open...ai/riv/idVysledku
  • RIV/00216208:11640/09:00334167
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • constant gain adaptive learning; medium–scale DSGE model; DSGE-VAR (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • CZ - Česká republika
http://linked.open...ontrolniKodProRIV
  • [F014B3386D26]
http://linked.open...i/riv/nazevZdroje
  • CERGE-EI Working Paper Series
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • -
http://linked.open...iv/tvurceVysledku
  • Slobodyan, Sergey
  • Wouters, R.
http://linked.open...n/vavai/riv/zamer
issn
  • 1211-3298
number of pages
http://localhost/t...ganizacniJednotka
  • 11640
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