Essec\Faculty\Model\Contribution {#2216 ▼
#_index: "academ_contributions"
#_id: "10812"
#_source: array:26 [
"id" => "10812"
"slug" => "10812-relevant-parameter-changes-in-structural-break-models"
"yearMonth" => "2020-07"
"year" => "2020"
"title" => "Relevant parameter changes in structural break models"
"description" => "DUFAYS, A. et ROMBOUTS, J. (2020). Relevant parameter changes in structural break models. <i>Journal of Econometrics</i>, 217(1), pp. 46-78. 
DUFAYS, A. et ROMBOUTS, J. (2020). Relevant parameter changes in structural break models. <i>Journal "
"authors" => array:2 [
0 => array:3 [
"name" => "ROMBOUTS Jeroen"
"bid" => "B00469813"
"slug" => "rombouts-jeroen"
]
1 => array:1 [
"name" => "DUFAYS Arnaud"
]
]
"ouvrage" => ""
"keywords" => array:4 [
0 => "Shrinkage prior"
1 => "Structural break model"
2 => "Relevant parameter change"
3 => "Bayesian inference"
]
"updatedAt" => "2021-09-24 10:33:27"
"publicationUrl" => "https://doi.org/10.1016/j.jeconom.2019.10.008"
"publicationInfo" => array:3 [
"pages" => "46-78"
"volume" => "217"
"number" => "1"
]
"type" => array:2 [
"fr" => "Articles"
"en" => "Journal articles"
]
"support_type" => array:2 [
"fr" => "Revue scientifique"
"en" => "Scientific journal"
]
"countries" => array:2 [
"fr" => null
"en" => null
]
"abstract" => array:2 [
"fr" => "Structural break time series models, which are commonly used in macroeconomics and finance, capture unknown structural changes by allowing for abrupt changes to model parameters. However, many specifications suffer from an over-parametrization issue, since typically all parameters have to change when a break occurs. We introduce a sparse change-point model to detect which parameters change over time. We propose a shrinkage prior distribution, which controls model parsimony by limiting the number of parameters that change from one structural break to another. We develop a Bayesian sampler for inference on the sparse change-point model. An extensive simulation study based on AR, ARMA and GARCH processes highlights the excellent performance of the sampler. We provide several empirical applications including an out-of-sample forecasting exercise showing that the Sparse change-point framework compares favorably with other recent time-varying parameter processes. Structural break time series models, which are commonly used in macroeconomics and finance, capture "
"en" => "Structural break time series models, which are commonly used in macroeconomics and finance, capture unknown structural changes by allowing for abrupt changes to model parameters. However, many specifications suffer from an over-parametrization issue, since typically all parameters have to change when a break occurs. We introduce a sparse change-point model to detect which parameters change over time. We propose a shrinkage prior distribution, which controls model parsimony by limiting the number of parameters that change from one structural break to another. We develop a Bayesian sampler for inference on the sparse change-point model. An extensive simulation study based on AR, ARMA and GARCH processes highlights the excellent performance of the sampler. We provide several empirical applications including an out-of-sample forecasting exercise showing that the Sparse change-point framework compares favorably with other recent time-varying parameter processes. Structural break time series models, which are commonly used in macroeconomics and finance, capture "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-04-03T21:21:42.000Z"
"docTitle" => "Relevant parameter changes in structural break models"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/rombouts-jeroen">ROMBOUTS Jeroen</a>, DUFAYS Arnaud"
"docDescription" => "<span class="document-property-authors">ROMBOUTS Jeroen, DUFAYS Arnaud</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2020</span> <span class="document-property-authors">ROMBOUTS Jeroen, DUFAYS Arnaud</span><br><span class="docume "
"keywordList" => "<a href="#">Shrinkage prior</a>, <a href="#">Structural break model</a>, <a href="#">Relevant parameter change</a>, <a href="#">Bayesian inference</a> <a href="#">Shrinkage prior</a>, <a href="#">Structural break model</a>, <a href="#">Relevant parame "
"docPreview" => "<b>Relevant parameter changes in structural break models</b><br><span>2020-07 | Journal articles </span> <b>Relevant parameter changes in structural break models</b><br><span>2020-07 | Journal articles </s "
"docType" => "research"
"publicationLink" => "<a href="https://doi.org/10.1016/j.jeconom.2019.10.008" target="_blank">Relevant parameter changes in structural break models</a> <a href="https://doi.org/10.1016/j.jeconom.2019.10.008" target="_blank">Relevant parameter changes i "
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.564305
+"parent": null
}
