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Activity Number: 167 - Data Mining and Econometrics
Type: Contributed
Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
Sponsor: Business and Economic Statistics Section
Abstract #318269
Title: An Application of the LASSO Regression to Assess Poverty on ECOWAS Countries
Author(s): Brian William Sloboda* and Dennis Pearson
Companies: Uop, Depart of Labor and Austin Peay State University
Keywords: LASSO; Poverty ; ECOWAS ; Big Data
Abstract:

The study of poverty has been studied from several different types of research approaches over the years. We use a statistical approach while economic theory is used to justify the inclusion of the variables used to assess poverty. Then, we interpret the empirical results and assess the validity of the model as applied to the Economic Community of West African States(ECOWAS) countries. More specifically, we use the LASSO regression to obtain sparse solutions to regressions problems. In fact, the lasso regression has numerous applications in disciplines from biology to economics. This analysis intends to determine which variables tell us about poverty in ECOWAS countries. Many ECOWAS countries have been recording 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 want to explore statistically the ECOWAS countries and show both whether economic theory and intuition are consistent with the data and attempt to infer results that examine poverty in ECOWAS and to consider additional future applications of the LASSO regression as it relates to poverty.


Authors who are presenting talks have a * after their name.

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