Mostrar el registro sencillo del ítem
dc.contributor.author
Cavallo, Alberto
dc.contributor.author
Cruces, Guillermo Antonio
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.contributor.author
Perez-Truglia, Ricardo
dc.date.available
2019-10-09T17:31:47Z
dc.date.issued
2016-03
dc.identifier.citation
Cavallo, Alberto; Cruces, Guillermo Antonio; Perez-Truglia, Ricardo; Learning from potentially biased statistics; Brookings Institution Press; Brookings Papers on Economic Activity; 2016; SPRING; 3-2016; 59-108
dc.identifier.issn
0007-2303
dc.identifier.uri
http://hdl.handle.net/11336/85462
dc.description.abstract
When forming expectations, households may be influenced by perceived bias in the information they receive. In this paper, we study how individuals learn from potentially biased statistics using data from both a natural experiment and a survey experiment during a period (2007-15) when the government of Argentina was manipulating official inflation statistics. This period is interesting because attention was being given to inflation information and both official and unofficial statistics were available. Our evidence suggests that, rather than ignoring biased statistics or naively accepting them, households react in a sophisticated way, as predicted by a Bayesian learning model. We also find evidence of an asymmetric reaction to inflation signals, with expectations changing more when the inflation rate rises than when it falls. These results could also be useful for understanding the formation of inflation expectations in less extreme contexts than Argentina, such as the United States and Europe, where experts may agree that statistics are unbiased but households are not.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Brookings Institution Press
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Expectations
dc.subject
Households
dc.subject
Biased statistics
dc.subject
Experiment
dc.subject.classification
Economía, Econometría
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.subject.classification
Economía y Negocios
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.subject.classification
CIENCIAS SOCIALES
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.title
Learning from potentially biased statistics
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-09-27T14:28:57Z
dc.identifier.eissn
1533-4465
dc.journal.volume
2016
dc.journal.number
SPRING
dc.journal.pagination
59-108
dc.journal.pais
Estados Unidos
![Se ha confirmado la validez de este valor de autoridad por un usuario](/themes/CONICETDigital/images/authority_control/invisible.gif)
dc.description.fil
Fil: Cavallo, Alberto. Massachusetts Institute of Technology; Estados Unidos
dc.description.fil
Fil: Cruces, Guillermo Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Económicas. Departamento de Ciencias Económicas. Centro de Estudios Distributivos Laborales y Sociales; Argentina
dc.description.fil
Fil: Perez-Truglia, Ricardo. Microsoft Research; Estados Unidos
dc.journal.title
Brookings Papers on Economic Activity
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://muse.jhu.edu/article/629296/summary
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1353/eca.2016.0013
Archivos asociados