Predicting surprises to GDP : a comparison of econometric and machine learning techniques
Thesis: M. Fin., Massachusetts Institute of Technology, Sloan School of Management, Master of Finance Program, 2017.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2017
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| Online Access: | http://hdl.handle.net/1721.1/109649 |
| _version_ | 1826214000258449408 |
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| author | Rajkumar, Ved |
| author2 | Roberto Rigobon. |
| author_facet | Roberto Rigobon. Rajkumar, Ved |
| author_sort | Rajkumar, Ved |
| collection | MIT |
| description | Thesis: M. Fin., Massachusetts Institute of Technology, Sloan School of Management, Master of Finance Program, 2017. |
| first_indexed | 2024-09-23T15:58:12Z |
| format | Thesis |
| id | mit-1721.1/109649 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:58:12Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1096492019-04-12T21:54:43Z Predicting surprises to GDP : a comparison of econometric and machine learning techniques Predicting surprises to gross domestic product : a comparison of econometric and machine learning techniques Comparison of econometric and machine learning techniques Rajkumar, Ved Roberto Rigobon. Sloan School of Management. Sloan School of Management. Sloan School of Management. 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). 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. by Ved Rajkumar. M. Fin. 2017-06-06T19:23:26Z 2017-06-06T19:23:26Z 2017 2017 Thesis http://hdl.handle.net/1721.1/109649 987002546 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 37 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Sloan School of Management. Rajkumar, Ved Predicting surprises to GDP : a comparison of econometric and machine learning techniques |
| title | Predicting surprises to GDP : a comparison of econometric and machine learning techniques |
| title_full | Predicting surprises to GDP : a comparison of econometric and machine learning techniques |
| title_fullStr | Predicting surprises to GDP : a comparison of econometric and machine learning techniques |
| title_full_unstemmed | Predicting surprises to GDP : a comparison of econometric and machine learning techniques |
| title_short | Predicting surprises to GDP : a comparison of econometric and machine learning techniques |
| title_sort | predicting surprises to gdp a comparison of econometric and machine learning techniques |
| topic | Sloan School of Management. |
| url | http://hdl.handle.net/1721.1/109649 |
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