Cheap talk and costly consequences

In this thesis, we use data on political interactions between country pairs to predict changes in trade. We implemented and applied a new feature selection algorithm called Boruta to build a compact set of predictor variables for this task. After finding a consistent set of features we used a Random Forest Classifier to predict bilateral changes in trade between 1998-2014. To better understand the contribution of each of the predictor variables used in the model we employ three different methods for calculating feature importance. Our results suggest that political and diplomatic interaction at at least as important (if not more) as distance and for predicting changes in trade.

Name thesis:degree_name

Master

Department dc:contributor.department

Program in Media Arts and Sciences (Massachusetts Institute of Technology)

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Loaiza Saa, Isabella.

Advisor dc:contributor.advisor

• Alex ('Sandy') Pentland.

• Program in Media Arts and Sciences

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/124082

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/124082