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PRODID:-//AMSE//Event Calendar//FR
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UID:event-8453@www.amse-aixmarseille.fr
DTSTAMP:20260502T013025Z
CREATED:20260502T013025Z
LAST-MODIFIED:20260502T013025Z
STATUS:CONFIRMED
SEQUENCE:0
SUMMARY:big data and econometrics seminar - Luc Bauwens
DTSTART:20211123T130000Z
DTEND:20211123T143000Z
DESCRIPTION:A large dimensional network or system can generate long memory 
 in its components\, as shown by Chevillon\, Hecq and Laurent (2018\, CHL) a
 nd Schennach (2018). These authors derive conditions under which the variab
 les generated by an infinite dimensional vector autoregressive model of ord
 er 1\, a VAR(1)\, exhibit long memory. We go one step further and show how 
 these asymptotic results can be put to practice for finite sample modeling 
 and inference regarding series with long range dependence that belong a net
 work or a large system. We propose to use a VAR(1)\, or an AR(1)-X when the
  VAR(1) model is estimated equation by equation\, whose parameters we shrin
 k to generic conditions matching those of CHL and Schennach. Our proposal s
 ignificantly outperforms ARFIMA and HAR models when forecasting a non-param
 etric estimate of the log of the integrated variance (i.e.\, log(MedRV)) of
  250 assets\, the annual productivity growth recorded in 100 industrial sec
 tors in the U.S.\, as well as seasonally adjusted historic monthly streamfl
 ow series recorded in 97 localizations of the Columbia river basin.\\n\\nCo
 ntact: Michel Lubrano : michel.lubrano[at]univ-amu.frPierre Michel : pierr
 e.michel[at]univ-amu.fr\n\nPlus d'informations: https://www.amse-aixmarseil
 le.fr/fr/evenements/luc-bauwens-1
LOCATION:Îlot Bernard du Bois - Salle 21\, AMU - AMSE\, 5-9 boulevard Maur
 ice Bourdet\, 13001 Marseille
URL;VALUE=URI:https://www.amse-aixmarseille.fr/fr/evenements/luc-bauwens-1
CONTACT:Michel Lubrano : michel.lubrano[at]univ-amu.frPierre Michel :&nbsp\
 ;pierre.michel[at]univ-amu.fr
TRANSP:OPAQUE
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