Open AccessImproving " Jackknife Instrumental Variable Estimation method" using A class of immun algorithm with practical application
Author(s) -
صباح منفي رضا,
علاء حسين صبري
Publication year - 2019
Publication title -
mağallaẗ al-ʿulūm al-iqtiṣādiyyaẗ wa-al-idāriyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2518-5764
pISSN - 2227-703X
DOI - 10.33095/jeas.v25i113.1707
Subject(s) - jackknife resampling , instrumental variable , estimator , computer science , algorithm , variable (mathematics) , class (philosophy) , mathematics , statistics , mathematical optimization , artificial intelligence , mathematical analysis
Most of the robust methods based on the idea of sacrificing one side versus promotion of another, the artificial intelligence mechanisms try to balance weakness and strength to make the best solutions in a random search technique. In this paper, a new idea is introduced to improve the estimators of parameters of linear simultaneous equation models that resulting from the Jackknife Instrumental Variable Estimation method (JIVE) by using a class of immune algorithm which called Clonal Selection Algorithm (CSA) and better estimates are obtained using one of the robust criterion which is called Mean Absolut Percentage Error (MAPE). The success of intelligence algorithm mechanisms has been proven that used to improve the parameters of linear simultaneous equation models according to user criterion and real data of size n=48.