Neural nets for indirect inference

For simulable models, neural networks are used to approximate the limited information posterior mean, which conditions on a vector of statistics, rather than on the full sample. Because the model is simulable, training and testing samples may be generated with sizes large enough to train well a net...

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Autor: Creel, Michael|||0000-0002-0944-8405
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:174326
Acceso en línea:https://ddd.uab.cat/record/174326
https://dx.doi.org/urn:doi:10.1016/j.ecosta.2016.11.008
Access Level:acceso abierto
Palabra clave:Neural networks
Indirect inference
Approximate Bayesian computing
Machine learning
DSGE
Jump-diffusion
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spelling Neural nets for indirect inferenceCreel, Michael|||0000-0002-0944-8405Neural networksIndirect inferenceApproximate Bayesian computingMachine learningDSGEJump-diffusionFor simulable models, neural networks are used to approximate the limited information posterior mean, which conditions on a vector of statistics, rather than on the full sample. Because the model is simulable, training and testing samples may be generated with sizes large enough to train well a net that is large enough, in terms of number of hidden layers and neurons, to learn the limited information posterior mean with good accuracy. Targeting the limited information posterior mean using neural nets is simpler, faster, and more successful than is targeting the full information posterior mean, which conditions on the observed sample. The output of the trained net can be used directly as an estimator of the model's parameters, or as an input to subsequent classical or Bayesian indirect inference estimation. The methods are illustrated with applications to a small dynamic stochastic general equilibrium model and a continuous time jump-diffusion model for stock index returns. 22017-01-0120172017-01-01Articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/174326https://dx.doi.org/urn:doi:10.1016/j.ecosta.2016.11.008reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets.https://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:1743262026-06-06T12:50:31Z
dc.title.none.fl_str_mv Neural nets for indirect inference
title Neural nets for indirect inference
spellingShingle Neural nets for indirect inference
Creel, Michael|||0000-0002-0944-8405
Neural networks
Indirect inference
Approximate Bayesian computing
Machine learning
DSGE
Jump-diffusion
title_short Neural nets for indirect inference
title_full Neural nets for indirect inference
title_fullStr Neural nets for indirect inference
title_full_unstemmed Neural nets for indirect inference
title_sort Neural nets for indirect inference
dc.creator.none.fl_str_mv Creel, Michael|||0000-0002-0944-8405
author Creel, Michael|||0000-0002-0944-8405
author_facet Creel, Michael|||0000-0002-0944-8405
author_role author
dc.subject.none.fl_str_mv Neural networks
Indirect inference
Approximate Bayesian computing
Machine learning
DSGE
Jump-diffusion
topic Neural networks
Indirect inference
Approximate Bayesian computing
Machine learning
DSGE
Jump-diffusion
description For simulable models, neural networks are used to approximate the limited information posterior mean, which conditions on a vector of statistics, rather than on the full sample. Because the model is simulable, training and testing samples may be generated with sizes large enough to train well a net that is large enough, in terms of number of hidden layers and neurons, to learn the limited information posterior mean with good accuracy. Targeting the limited information posterior mean using neural nets is simpler, faster, and more successful than is targeting the full information posterior mean, which conditions on the observed sample. The output of the trained net can be used directly as an estimator of the model's parameters, or as an input to subsequent classical or Bayesian indirect inference estimation. The methods are illustrated with applications to a small dynamic stochastic general equilibrium model and a continuous time jump-diffusion model for stock index returns.
publishDate 2017
dc.date.none.fl_str_mv 2
2017-01-01
2017
2017-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
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dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/174326
https://dx.doi.org/urn:doi:10.1016/j.ecosta.2016.11.008
url https://ddd.uab.cat/record/174326
https://dx.doi.org/urn:doi:10.1016/j.ecosta.2016.11.008
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
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dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
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