Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "1751"
  #_source: array:26 [
    "id" => "1751"
    "slug" => "learning-can-generate-long-memory"
    "yearMonth" => "2017-05"
    "year" => "2017"
    "title" => "Learning can generate long memory"
    "description" => "CHEVILLON, G. et MAVROEIDIS, S. (2017). Learning can generate long memory. <i>Journal of Econometrics</i>, 198(1), pp. 1-9."
    "authors" => array:2 [
      0 => array:3 [
        "name" => "CHEVILLON Guillaume"
        "bid" => "B00072304"
        "slug" => "chevillon-guillaume"
      ]
      1 => array:1 [
        "name" => "MAVROEIDIS S."
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:4 [
      0 => "Long memory"
      1 => "Recursive least squares"
      2 => "Decreasing gain learning"
      3 => "New Keynesian Phillips curve"
    ]
    "updatedAt" => "2021-09-24 10:33:27"
    "publicationUrl" => "https://www.sciencedirect.com/science/article/abs/pii/S0304407617300027"
    "publicationInfo" => array:3 [
      "pages" => "1-9"
      "volume" => "198"
      "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" => "We study learning dynamics in a prototypical representative-agent forward-looking model in which agents’ beliefs are updated using linear learning algorithms. We show that learning in this model can generate long memory endogenously, without any persistence in the exogenous shocks, depending on the weights agents place on past observations when they update their beliefs, and on the magnitude of the feedback from expectations to the endogenous variable. This is distinctly different from the case of rational expectations, where the memory of the endogenous variable is determined exogenously."
      "en" => "We study learning dynamics in a prototypical representative-agent forward-looking model in which agents’ beliefs are updated using linear learning algorithms. We show that learning in this model can generate long memory endogenously, without any persistence in the exogenous shocks, depending on the weights agents place on past observations when they update their beliefs, and on the magnitude of the feedback from expectations to the endogenous variable. This is distinctly different from the case of rational expectations, where the memory of the endogenous variable is determined exogenously."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-12-03T18:21:44.000Z"
    "docTitle" => "Learning can generate long memory"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/chevillon-guillaume">CHEVILLON Guillaume</a>, MAVROEIDIS S."
    "docDescription" => "<span class="document-property-authors">CHEVILLON Guillaume, MAVROEIDIS S.</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2017</span>"
    "keywordList" => "<a href="#">Long memory</a>, <a href="#">Recursive least squares</a>, <a href="#">Decreasing gain learning</a>, <a href="#">New Keynesian Phillips curve</a>"
    "docPreview" => "<b>Learning can generate long memory</b><br><span>2017-05 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://www.sciencedirect.com/science/article/abs/pii/S0304407617300027" target="_blank">Learning can generate long memory</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.293978
  +"parent": null
}