SMU SOE-SKBI Seminar (Feb 5, 2020): Econometric Methods and Data Science Techniques: A Review of Two Strands of Literature and an Introduction to Hybrid Methods

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TOPIC:

ECONOMETRIC METHODS AND DATA SCIENCE TECHNIQUES: A REVIEW OF TWO STRANDS OF LITERATURE AND AN INTRODUCTION TO HYBRID METHODS

ABSTRACT

The data market has been growing at an exceptional pace. Consequently, more sophisticated strategies to conduct economic forecasts have been introduced using machine learning techniques. Does machine learning pose a threat to conventional econometric methods for the purpose of forecasting economic activities? Or does machine learning present great opportunities to cross-fertilize the field of the econometric forecast? In this report, we develop a pedagogical framework that identifies complementarities and build bridges between the two strands of literature. Existing econometric methods and machine learning techniques, for the purposed of economic forecasting, are reviewed and compared. The strength and weakness of these two classes of methods are discussed. A class of hybrid methods which combine econometrics and machine learning are introduced. New directions on combining the two classes of methods are suggested. Performance of alternative methods is compared in terms of Chicago Board Options Exchange's Volatility Index out-of-sample forecasting exercises.

Click here to view Professor Yu's CV.

Click here to view Professor Xie's CV.

PRESENTERS

Jun Yu & Tian Xie

SMU & Shanghai University

of Finance and Economics

RESEARCH FIELD

Econometrics

DATE:

5 February 2020 (Wednesday)

TIME:

4pm - 5.30pm

VENUE:

Meeting Room 5.1, Level 5
School of Economics
Singapore Management University
90 Stamford Road
Singapore 178903

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