Fuzzy multiple regressions for Cross-Section and Panel data

被引：0
|
作者：
Belhadj, Besma [1 ,2 ]
机构：
[1] Univ Tunis ElManar, LaReQuad, FSEGT, Tunis, Tunisia
[2] IHE Tunis, LIRS, Tunis, Tunisia
来源：
SOCIO-ECONOMIC PLANNING SCIENCES | 2024年 / 91卷
关键词：
Fuzzy endogenous regressor Fuzzy parameters Fuzzy mathematical modeling Cross-sectional data Panel data; MODELS; INPUT;
D O I：
10.1016/j.seps.2023.101761
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
A classical multiple regression is a framework defined a priori verifying several limiting assumptions and easily misused. We propose a fuzzy alternative approach to classical multiple regressions for cross-sectional and panel data. As illustration, we estimate and analyze the effect of the annual GDP growth rate, unemployment rate, inflation rate and annual population growth rate on poverty in the Middle East and North Africa (MENA) region.
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页数：13
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