Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/245414 
Year of Publication: 
2021
Series/Report no.: 
CESifo Working Paper No. 9233
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
While traditional empirical models using determinants like size and trade costs are able to predict RTA formation reasonably well, we demonstrate that allowing for machine detected non-linear patterns helps to improve the predictive power of RTA formation substantially. We employ machine learning methods and find that the fitted tree-based methods and neural networks deliver sharper and more accurate predictions than the probit model. For the majority of models the allowance of fixed effects increases the predictive performance considerably. We apply our models to predict the likelihood of RTA formation of the EU and the United States with their trading partners, respectively.
Subjects: 
Regional Trade Agreements
neural networks
tree-based methods
high-dimensional fixed effects
JEL: 
F14
F15
C45
C53
Document Type: 
Working Paper
Appears in Collections:

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.