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Big data analytics in economics: What have we learned so far, and where should we go from here?

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  • Norman R. Swanson
  • Weiqi Xiong

Abstract

Research into predictive accuracy testing remains at the forefront of the forecasting field. One reason for this is that rankings of predictive accuracy across alternative models, which under misspecification are loss function dependent, are universally utilized to assess the usefulness of econometric models. A second reason, which corresponds to the objective of this paper, is that researchers are currently focusing considerable attention on so‐called big data and on new (and old) tools that are available for the analysis of this data. One of the objectives in this field is the assessment of whether big data leads to improvement in forecast accuracy. In this survey paper, we discuss some of the latest (and most interesting) methods currently available for analyzing and utilizing big data when the objective is improved prediction. Our discussion includes a summary of various so‐called dimension reduction, shrinkage and machine learning methods as well as a summary of recent tools that are useful for ranking prediction models associated with the implementation of these methods. We also provide a brief empirical illustration of big data in action, in which we show that big data are indeed useful when predicting the term structure of interest rates. L’analyse des données massives en économie : ce qu’on a appris jusqu’à maintenant et quelles directions pour la prochaine étape? Les travaux sur les tests de précision des prévisions demeurent au premier plan dans le monde de la prévision. Une première raison est que les ordonnancements de la précision des prévisions des divers modèles (qui selon le degré de mis‐spécification dépend de la fonction de perte) sont universellement utilisés pour calibrer l’utilité des modèles économétriques. Une seconde raison, qui correspond à l’objectif de ce mémoire, est que les chercheurs concentrent une portion considérable de leur attention sur ce qu’on appelle les données massives, et les outils (anciens et nouveaux) disponibles pour analyser ce type de données. Un des objectifs dans ce champ d’études est d’établir si les données massives mènent à l’amélioration dans la précision des prévisions. Dans cette étude‐synthèse, les auteurs examinent quelques‐unes des méthodes les plus récentes et les plus intéressantes qui sont disponibles pour analyser et utiliser ce genre de données quand l’objectif est d’améliorer les prévisions. La discussion inclut une présentation succincte d’approches en termes de réduction de dimensions, de rétrécissement, et méthodes d’apprentissage machine, ainsi qu’un résumé succinct d’outillages récents qui sont utiles pour ordonnancer les modèles de prévision associés à la mise en application de ces méthodes. On fournit aussi une brève illustration de données massives en action, dans laquelle on montre que l’analyse des données massives est utile pour prévoir la structure par échéance des taux d’intérêt.

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  • Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(3), pages 695-746, August.
  • Handle: RePEc:wly:canjec:v:51:y:2018:i:3:p:695-746
    DOI: 10.1111/caje.12336
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    6. Jennifer Castle & Jurgen Doornik & David Hendry, 2020. "Modelling Non-stationary 'Big Data'," Economics Series Working Papers 905, University of Oxford, Department of Economics.

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