Essec\Faculty\Model\Contribution {#2216 ▼
#_index: "academ_contributions"
#_id: "12760"
#_source: array:26 [
"id" => "12760"
"slug" => "12760-sparse-change%e2%80%90point-var-models"
"yearMonth" => "2021-09"
"year" => "2021"
"title" => "Sparse change‐point VAR models"
"description" => "DUFAYS, A., LI, Z., ROMBOUTS, J. et SONG, Y. (2021). Sparse change‐point VAR models. <i>Journal of Applied Econometrics</i>, 36(6), pp. 703-727. 
DUFAYS, A., LI, Z., ROMBOUTS, J. et SONG, Y. (2021). Sparse change‐point VAR models. <i>Journal of A "
"authors" => array:4 [
0 => array:3 [
"name" => "ROMBOUTS Jeroen"
"bid" => "B00469813"
"slug" => "rombouts-jeroen"
]
1 => array:1 [
"name" => "DUFAYS Arnaud"
]
2 => array:1 [
"name" => "LI Zhuo"
]
3 => array:1 [
"name" => "SONG Yong"
]
]
"ouvrage" => ""
"keywords" => array:1 [
0 => "VAR models"
]
"updatedAt" => "2023-05-31 10:52:44"
"publicationUrl" => "https://doi.org/10.1002/jae.2844"
"publicationInfo" => array:3 [
"pages" => "703-727"
"volume" => "36"
"number" => "6"
]
"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" => "Change-point (CP) VAR models face a dimensionality curse due to the proliferation of parameters that arises when new breaks are detected. We introduce the Sparse CP-VAR model which determines which parameters truly vary when a break is detected. By doing so, the number of new parameters to be estimated at each regime is drastically reduced and the break dynamics becomes easier to be interpreted. The Sparse CP-VAR model disentangles the dynamics of the mean parameters and the covariance matrix. The former uses CP dynamics with shrinkage prior distributions, while the latter is driven by an infinite hidden Markov framework. An extensive simulation study is carried out to compare our approach with existing ones. We provide applications to financial and macroeconomic systems. Change-point (CP) VAR models face a dimensionality curse due to the proliferation of parameters that "
"en" => "Change-point (CP) VAR models face a dimensionality curse due to the proliferation of parameters that arises when new breaks are detected. We introduce the Sparse CP-VAR model which determines which parameters truly vary when a break is detected. By doing so, the number of new parameters to be estimated at each regime is drastically reduced and the break dynamics becomes easier to be interpreted. The Sparse CP-VAR model disentangles the dynamics of the mean parameters and the covariance matrix. The former uses CP dynamics with shrinkage prior distributions, while the latter is driven by an infinite hidden Markov framework. An extensive simulation study is carried out to compare our approach with existing ones. We provide applications to financial and macroeconomic systems. Change-point (CP) VAR models face a dimensionality curse due to the proliferation of parameters that "
]
"authors_fields" => array:2 [
"fr" => "Systèmes d'Information, Data Analytics et Opérations"
"en" => "Information Systems, Data Analytics and Operations"
]
"indexedAt" => "2025-04-04T00:21:43.000Z"
"docTitle" => "Sparse change‐point VAR models"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/rombouts-jeroen">ROMBOUTS Jeroen</a>, DUFAYS Arnaud, LI Zhuo, SONG Yong"
"docDescription" => "<span class="document-property-authors">ROMBOUTS Jeroen, DUFAYS Arnaud, LI Zhuo, SONG Yong</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2021</span> <span class="document-property-authors">ROMBOUTS Jeroen, DUFAYS Arnaud, LI Zhuo, SONG Yong</span><br "
"keywordList" => "<a href="#">VAR models</a>"
"docPreview" => "<b>Sparse change‐point VAR models</b><br><span>2021-09 | Journal articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://doi.org/10.1002/jae.2844" target="_blank">Sparse change‐point VAR models</a>"
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.433686
+"parent": null
}
