Comparative Analysis of Foreign Direct Investments and Remittances to Five English-Speaking West African Countries Using Statistical, Machine Learning and Deep learning Models

Project Information

Project Status: Recruiting
Project Region: CAREERS
Submitted By: Carrie Brown
Project Email: snwoji@harrisburgu.edu

Preferred Start Date: As soon as possible.

Mentors: Iheb Abdellatif
Students: Recruiting
Student Skill Level Required: The student facilitator must have the following skills:
1. High emotional intelligence to work with other students, the mentor, and the PI.
2. Proficiency in modeling data using econometric, machine learning, and deep learning models.
3. Good writing and communication skills.

Project Description

The five English-speaking West African countries, Ghana, Gambia, Liberia, Nigeria, and Sierra Leone
have benefited from both foreign direct investments and remittances from their citizens in diaspora.
Existing scholarship on remittances and foreign direct investments have been purely econometric with
particular emphasis on the relationship of these foreign sources of income with GDP of nations (Tahir,
Khan, & Shar, 2015; Comes, Bunduchi, Vasile, & Stefan, 2018; Minh, 2020; Salisu, 2020). This approach
has helped scholars and practitioners understand the impact of remittances and foreign direct investments
on the economy of nations. However, these studies have been made in silo and there is a dearth of
literature on the comparative analysis of the yearly inflow of foreign direct investments and remittances to
the five English-speaking West African countries. The assumptions are that migration from these
countries leads to brain drain (Idemudia & Boehnke, 2020; Awire & Okumagba, 2020; Fofack &
Akendung, 2020) and that these developing countries depend on foreign direct investments to exist
(Shittu, Yusuf, El Houssein, & Hassan, 2020; Appiah-Kubi, et al., 2020). This study will therefore
compare the inflow of foreign direct investments and remittances to this economic bloc to understand the
impact of both to the region. Moreover, the present studies are mostly done by using econometric models.
In this study, econometric, machine learning, and deep learning models will be used both to compare and
forecast foreign direct investments and remittances.

Project Information

Project Status: Recruiting
Project Region: CAREERS
Submitted By: Carrie Brown
Project Email: snwoji@harrisburgu.edu

Preferred Start Date: As soon as possible.

Mentors: Iheb Abdellatif
Students: Recruiting
Student Skill Level Required: The student facilitator must have the following skills:
1. High emotional intelligence to work with other students, the mentor, and the PI.
2. Proficiency in modeling data using econometric, machine learning, and deep learning models.
3. Good writing and communication skills.

Project Description

The five English-speaking West African countries, Ghana, Gambia, Liberia, Nigeria, and Sierra Leone
have benefited from both foreign direct investments and remittances from their citizens in diaspora.
Existing scholarship on remittances and foreign direct investments have been purely econometric with
particular emphasis on the relationship of these foreign sources of income with GDP of nations (Tahir,
Khan, & Shar, 2015; Comes, Bunduchi, Vasile, & Stefan, 2018; Minh, 2020; Salisu, 2020). This approach
has helped scholars and practitioners understand the impact of remittances and foreign direct investments
on the economy of nations. However, these studies have been made in silo and there is a dearth of
literature on the comparative analysis of the yearly inflow of foreign direct investments and remittances to
the five English-speaking West African countries. The assumptions are that migration from these
countries leads to brain drain (Idemudia & Boehnke, 2020; Awire & Okumagba, 2020; Fofack &
Akendung, 2020) and that these developing countries depend on foreign direct investments to exist
(Shittu, Yusuf, El Houssein, & Hassan, 2020; Appiah-Kubi, et al., 2020). This study will therefore
compare the inflow of foreign direct investments and remittances to this economic bloc to understand the
impact of both to the region. Moreover, the present studies are mostly done by using econometric models.
In this study, econometric, machine learning, and deep learning models will be used both to compare and
forecast foreign direct investments and remittances.