An application of the LASSO and elastic net regression to assess poverty and economic freedom on ECOWAS countries

Brian W. Sloboda; Dennis Pearson; Madi Etherton; Brian W. Sloboda; Dennis Pearson; Madi Etherton

doi:10.3934/mbe.2023541

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 7: 12154-12168. doi: 10.3934/mbe.2023541

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An application of the LASSO and elastic net regression to assess poverty and economic freedom on ECOWAS countries

1.
Department of Economics and Finance, University of Maryland, Global Campus, USA
2.
Austin Peay State University, Clarksville, TN, USA

Academic Editor: Jeffrey Yi-Lin Forrest

Received: 25 October 2022 Revised: 05 February 2023 Accepted: 27 February 2023 Published: 16 May 2023

The study of poverty has been studied from several different research approaches over the years. This analysis intended to determine which variables tell us about poverty in the Economic Community of West African State (ECOWAS) countries. Many ECOWAS countries have recorded high economic growth rates in the past few decades. However, a recent trend is that this progress is reversing, and poverty rates are increasing. In this analysis, we examined the variables describing poverty in ECOWAS. We used a statistical approach coupled with economic theory to justify the inclusion of the variables used to assess poverty. Furthermore, we include the use of the Fraser Institute's Economic Freedom index for each of the West African States. As far as we know, the economic freedom and poverty of West African states have not been presented for consideration toward African growth rates and poverty rates. With few exceptions, economic freedom research suggests that economic freedom is the foundational ingredient for increasing prosperity and reducing poverty. Then, we interpreted the empirical results and assess the validity of the model as applied to the ECOWAS countries. More specifically, we use the LASSO and elastic net regression to obtain sparse solutions to regression problems. LASSO and elastic net are computational methods that rapidly inform us about the relevant variables for the model. These computational methods' performances will in the context of the number of variables exceeds the number of observations by generating a low mean squared error (MSE).
- LASSO,
- elastic net,
- cross validation,
- poverty,
- ECOWAS,
- economic freedom
Citation: Brian W. Sloboda, Dennis Pearson, Madi Etherton. An application of the LASSO and elastic net regression to assess poverty and economic freedom on ECOWAS countries[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 12154-12168. doi: 10.3934/mbe.2023541

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Abstract

The study of poverty has been studied from several different research approaches over the years. This analysis intended to determine which variables tell us about poverty in the Economic Community of West African State (ECOWAS) countries. Many ECOWAS countries have recorded high economic growth rates in the past few decades. However, a recent trend is that this progress is reversing, and poverty rates are increasing. In this analysis, we examined the variables describing poverty in ECOWAS. We used a statistical approach coupled with economic theory to justify the inclusion of the variables used to assess poverty. Furthermore, we include the use of the Fraser Institute's Economic Freedom index for each of the West African States. As far as we know, the economic freedom and poverty of West African states have not been presented for consideration toward African growth rates and poverty rates. With few exceptions, economic freedom research suggests that economic freedom is the foundational ingredient for increasing prosperity and reducing poverty. Then, we interpreted the empirical results and assess the validity of the model as applied to the ECOWAS countries. More specifically, we use the LASSO and elastic net regression to obtain sparse solutions to regression problems. LASSO and elastic net are computational methods that rapidly inform us about the relevant variables for the model. These computational methods' performances will in the context of the number of variables exceeds the number of observations by generating a low mean squared error (MSE).

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