Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
PREDICTING EXTERNAL MACROECONOMIC CRISES: MACHINE LEARNING PERSPECTIVE
Download
PREDICTING EXTERNAL MACROECONOMIC CRISES MACHINE LEARNING PERSPECTIVE.pdf
Date
2024-3-06
Author
CELIK, Songul Siva
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
158
views
138
downloads
Cite This
In this study, our primary objective is to predict Sudden Stops using Machine Learning (ML) methods and evaluate their out-of-sample prediction power. Conducted in two phases, the first involves establishing a baseline with Forbes and Warnock (2021) as our model, replicating and assessing its out-of-sample predictions. We then introduce various ML methods, including Elastic Net, Random Forests, Support Vector Machines, kNN, AdaBoost, XGBoost, and Multi-Layer Perceptron, for a comprehensive comparison against the baseline, utilizing metrics like accuracy, precision, recall, F1-score, and ROC curve. In the second phase, we expand the dataset from the IMF, prioritizing data availability, and employ ML methods for feature selection. Selected features are used for ML estimation, involving traditional methods like Elastic Net, Random Forests, Support Vector Machines, XGBoost, Logistic Regression, and the modern deep learning method, a Long Short-Term Memory (LSTM).The analysis aims to explore potential improvements in out-of-sample performances facilitated by ML algorithms and feature selection.
Subject Keywords
Machine Learning
,
Sudden Stops
,
Deep Learning
,
Out of Sample Prediction
URI
https://hdl.handle.net/11511/109045
Collections
Graduate School of Social Sciences, Thesis
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
S. S. CELIK, “PREDICTING EXTERNAL MACROECONOMIC CRISES: MACHINE LEARNING PERSPECTIVE,” Ph.D. - Doctoral Program, Middle East Technical University, 2024.