Forecasting Euro Area Macroeconomic Variables with Bayesian Adaptive Elastic Net
Forecasting Euro Area Macroeconomic Variables with Bayesian Adaptive Elastic Net
Date
2015
Authors
Editors
Journal ISSN
Electronic ISSN
ISBN
Bibliographical data
Publisher
Series
Working Paper Series / Department of Economics; 2015-12
URI (citable link)
International patent number
Link to the license
EU project number
Project
Open Access publication
Collections
Title in another language
Publication type
Working Paper/Technical Report
Publication status
Published
Published in
Abstract
I use the adaptive elastic net in a Bayesian framework and test its forecasting performance against lasso, adaptive lasso and elastic net (all used in a Bayesian framework) in a series of simulations, as well as in an empirical exercise for macroeconomic Euro area data. The results suggest that elastic net is the best model among the four Bayesian methods considered. Adaptive lasso, on the other hand, shows the worst forecasting performance. Lasso is generally better then adaptive lasso, but worse than adaptive elastic net. The differences in the performance of these models become especially large when the number of regressors grows considerably relative to the number of available observations. The results point to the fact that the ridge regression component in the elastic net is responsible for its improvement in forecasting performance over lasso. The adaptive shrinkage in some of the models does not seem to play a major role, and may even lead to a deterioration of the performance.
Summary in another language
Subject (DDC)
330 Economics
Keywords
Elastic net, Lasso, Bayesian, Forecasting
Conference
Review
undefined / . - undefined, undefined. - (undefined; undefined)
Cite This
ISO 690
STANKIEWICZ, Sandra, 2015. Forecasting Euro Area Macroeconomic Variables with Bayesian Adaptive Elastic NetBibTex
@techreport{Stankiewicz2015Forec-32745, year={2015}, series={Working Paper Series / Department of Economics}, title={Forecasting Euro Area Macroeconomic Variables with Bayesian Adaptive Elastic Net}, number={2015-12}, author={Stankiewicz, Sandra} }
RDF
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/32745"> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/46"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2016-01-28T09:45:21Z</dcterms:available> <dc:contributor>Stankiewicz, Sandra</dc:contributor> <dc:language>eng</dc:language> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/32745/3/Stankiewicz_0-319666.pdf"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2016-01-28T09:45:21Z</dc:date> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/32745"/> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/32745/3/Stankiewicz_0-319666.pdf"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/46"/> <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/> <dc:rights>terms-of-use</dc:rights> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dcterms:issued>2015</dcterms:issued> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:title>Forecasting Euro Area Macroeconomic Variables with Bayesian Adaptive Elastic Net</dcterms:title> <dc:creator>Stankiewicz, Sandra</dc:creator> <dcterms:abstract xml:lang="eng">I use the adaptive elastic net in a Bayesian framework and test its forecasting performance against lasso, adaptive lasso and elastic net (all used in a Bayesian framework) in a series of simulations, as well as in an empirical exercise for macroeconomic Euro area data. The results suggest that elastic net is the best model among the four Bayesian methods considered. Adaptive lasso, on the other hand, shows the worst forecasting performance. Lasso is generally better then adaptive lasso, but worse than adaptive elastic net. The differences in the performance of these models become especially large when the number of regressors grows considerably relative to the number of available observations. The results point to the fact that the ridge regression component in the elastic net is responsible for its improvement in forecasting performance over lasso. The adaptive shrinkage in some of the models does not seem to play a major role, and may even lead to a deterioration of the performance.</dcterms:abstract> </rdf:Description> </rdf:RDF>