Publication Date
5-2022
Date of Final Oral Examination (Defense)
5-7-2022
Type of Culminating Activity
Thesis
Degree Title
Master of Science in Economics
Department
Economics
Supervisory Committee Chair
Michail Fragkias, Ph.D.
Supervisory Committee Member
Jayash Paudel, Ph.D.
Supervisory Committee Member
Samia Islam, Ph.D.
Abstract
Gross metropolitan product (GMP) is one the most critical indicators for determining a metropolitan area’s economic performance. While GMP data currently exists for major cities in the US and OECD countries, the rest of the world is a blind spot. This study aims at estimating the GMP of 1289 cities in non-US and OECD countries, where no official city-level statistics are produced. We perform this estimation through multiple machine learning models, using night-time lights satellite imagery, and other publicly available data. We analyze eight spatial databases and four cross-sectional datasets and derive a feature vector of covariates through various techniques, i.e., downscaling and bootstrap. We specify OLS, Ridge, Lasso, Elastic Net, and Random Forest models, out of which Random Forest generated the most accurate results with 0.3 RMSE for out-of-sample predictions. With this methodology, we produced the first existing data set that groups the 1298 cities into 20 quantiles, with the first quantile denoting the lowest five percent regarding estimated income and the twentieth quantile denoting the highest five percent regarding the estimated economic product.
DOI
https://doi.org/10.18122/td.1934.boisestate
Recommended Citation
Joshaghani, Simin, "Predicting Gross Metropolitan Product Worldwide Using Statistical Learning Models, Socio-Economic, and Satellite Imagery Data" (2022). Boise State University Theses and Dissertations. 1934.
https://doi.org/10.18122/td.1934.boisestate