Predicting surprises to GDP : a comparison of econometric and machine learning techniques
Name
987002546-MIT.pdf
Description
Full printable version
Size
3.63 MB
Format
Adobe PDF
Checksum (MD5)
b1b63f90cb611085988f8d4e46e041b1
Author(s)
Rajkumar, Ved
Advisor(s)
Roberto Rigobon.
Alternative Title
Predicting surprises to gross domestic product : a comparison of econometric and machine learning techniques
Comparison of econometric and machine learning techniques
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
This study takes its inspiration from the practice of nowcasting, which involves making short horizon forecasts of specific data items, typically GDP growth in the context of economics. We alter this approach by targeting surprises to GDP growth, where the expectation is defined as the consensus estimate of economists and a surprise is a deviation of the realized value from the expectation. We seek to determine if surprises are predictable at a better than random rate through the use of four statistical techniques: OLS, logit, random forest, and neural network. In addition to evaluating predictability we also seek to compare the four techniques, the former two of which are common in econometric literature and the latter two of which are machine learning algorithms most commonly seen in engineering settings. We find that the neural network technique predicts surprises at an encouraging rate, and while the results are not overwhelmingly positive they do suggest that the model may identify relationships in the data that elude the consensus.
Description
Thesis: M. Fin., Massachusetts Institute of Technology, Sloan School of Management, Master of Finance Program, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 35).
Subjects
Sloan School of Management.
MIT Department
Sloan School of Management
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