Python for unified research in econometrics and statistics

Abstract : Python is a powerful high level open source programming language, that is available for multiple platforms. It supports object oriented programming, and has recently become a serious alternative to low level compiled languages such as C. It is easy to learn and use, and is recognized for very fast development times, which makes it suitable for rapid software prototyping as well as teaching purposes. We motivate the use of Python and its free extension modules for high performance stand alone applications in econometrics and statistics, and as a tool for gluing different applications together. We give details on the core language features, which will enable a user to immediately begin work, and then provide practical examples of advanced uses of Python. Finally, we compare the run-time performance of extended Python against a number of commonly used statistical packages and programming environments.
Type de document :
Article dans une revue
Econometric Reviews, Taylor & Francis, 2012, 31 (5), pp 558-591. 〈10.1080/07474938.2011.553573〉
Liste complète des métadonnées

Littérature citée [30 références]  Voir  Masquer  Télécharger

https://hal-enac.archives-ouvertes.fr/hal-01021587
Contributeur : Laurence Porte <>
Soumis le : vendredi 11 juillet 2014 - 16:15:49
Dernière modification le : jeudi 1 février 2018 - 11:18:01
Document(s) archivé(s) le : samedi 11 octobre 2014 - 10:45:37

Fichier

210.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Roseline Bilina, Steve Lawford. Python for unified research in econometrics and statistics. Econometric Reviews, Taylor & Francis, 2012, 31 (5), pp 558-591. 〈10.1080/07474938.2011.553573〉. 〈hal-01021587〉

Partager

Métriques

Consultations de la notice

1860

Téléchargements de fichiers

2133