Econometric Methods and Data Science Techniques: A Review of Two Strands of Literature and an Introduction to Hybrid Methods
Tian Xie (),
Jun Yu () and
Tao Zeng ()
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Tian Xie: Shanghai University of Finance and Economics
Tao Zeng: Zhejiang University
No 16-2020, Economics and Statistics Working Papers from Singapore Management University, School of Economics
The data market has been growing at an exceptional pace. Consequently, more sophisticated strategies to conduct economic forecasts have been introduced with machine learning techniques. Does machine learning pose a threat to conventional econometric methods in terms of forecasting? Moreover, does machine learning present great opportunities to cross-fertilize the field of econometric forecasting? In this report, we develop a pedagogical framework that identifies complementarity and bridges between the two strands of literature. Existing econometric methods and machine learning techniques for economic forecasting are reviewed and compared. The advantages and disadvantages of these two classes of methods are discussed. A class of hybrid methods that combine conventional econometrics and machine learning are introduced. New directions for integrating the above two are suggested. The out-of-sample performance of alternatives is compared when they are employed to forecast the Chicago Board Options Exchange Volatility Index and the harmonized index of consumer prices for the euro area. In the first exercise, econometric methods seem to work better, whereas machine learning methods generally dominate in the second empirical application.
Pages: 139 pages
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-for and nep-sea
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Persistent link: https://EconPapers.repec.org/RePEc:ris:smuesw:2020_016
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