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

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.Résumé. 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 participants of the 2017 Deutsche Bundesbank Workshop on Forecasting for useful comments and suggestions.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.JEL classification: C12, C22, C534 In their setu...

Predicting interest rates using shrinkage methods, real‐time diffusion indexes, and model combinations

“…Finally, we find that the use of real‐time data matters when forecasting interest rates using NS‐type models, in the sense that we overturn the findings of Swanson and Xiong (2018). In particular, using fully revised data, Swanson and Xiong find that big data such as that used in this paper “matter,” since variables other than yields enter into their MSFE‐best NS‐type models, even when forecast combinations are considered.…”

“…When combination forecasts are included in our analysis, thus, there are no unspanned risks (i.e., risks not spanned by bond or bound returns), and all relevant forecasting information is contained in the term structure. In the context of our experiments, this indicates that the use of fully revised macroeconomic data may have an important confounding effect upon results obtained when forecasting yields, as prior empirical evidence suggests that diffusion indexes are often useful for predicting yields when constructed using fully revised data, regardless of whether forecast combination is used, or not (see Swanson & Xiong, 2018). This suggests that one possible explanation for our finding concerning the usefulness of macroeconomic diffusion indexes pre Great Recession has to do with model misspecification, particularly since our MSFE‐best combination utilizes all of our purely yield driven models.…”

“…Several studies have revealed the importance of collecting and updating such real‐time data sets including Diebold and Rudebusch (1991, 2002a), Hamilton and Perez‐Quiros (1996), Bernanke and Boivin (2003), and the papers cited therein. We ask whether “information‐rich” diffusion indexes are useful for predicting yields, when the data used to construct the indexes are purely “real‐time” rather than fully revised, as in Swanson and Xiong (2018). In this sense, we revisit the finding of McCracken and Ng (2016) that diffusion indexes extracted from real‐time and “fully revised” data sets contain the same predictive content.…”

AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries

“…Hansen, Memahon, & Prat (2017) Saltzman & Yung (2018) showed that business and economic related uncertainty is related to future weakness in output, higher unemployment, and term premium by using natural language processing techniques. Swanson & Xiong (2018) indicated that big data are useful when predicting the term structure of interest rates.…”

Sources and Types of Big Data for Macroeconomic Forecasting

“…series of subsequent corrections. Today, high frequency economic time series allow researchers to produce and adjust their forecasts far more frequently (Baldacci et al, 2016;Bok, Caratelli, Giannone, Sbordone, & Tambalotti, 2018;Einav & Levin, 2014a, 2014bSwanson & Xiong, 2018), even in real-time (Croushore, 2011). Not only are today's time series updated more frequently, but there are more of them available than ever before (Bok et al, 2018) on a much more heterogeneous set of topics (Einav & Levin, 2014b), often with near population-level coverage (Einav & Levin, 2014a).…”