Abstract
Most robust methods are based on the principle of making a strategic trade-off, sacrificing certain aspects to strengthen others through various techniques. In contrast, artificial intelligence mechanisms attempt to balance strengths and weaknesses to reach optimal solutions via stochastic search methods. This research introduces a novel approach to improving the parameter estimators of linear simultaneous equation models derived from the Jackknife Instrumental Variable Estimation (JIVE) method. This is achieved by employing a specific class of the Immune Algorithm known as the Clonal Selection Algorithm (CSA). The results demonstrated improved estimators when evaluated using a robust performance criterion, specifically the Mean Absolute Percentage Error (MAPE). The findings confirm the effectiveness of the utilized AI mechanisms in enhancing the estimators of the linear simultaneous equation model, as evidenced by the adopted criterion and real-world data with a sample size of 48.
DOI
10.33095/jeas.v25i113.1707
Subject Area
Statistical
First Page
462
Last Page
474
Recommended Citation
Redha, S. M., & Sabri, A. H. (2019). Improving " Jackknife Instrumental Variable Estimation Method" Using a Class of IMMUN Algorithm with Practical Application. Journal of Economics and Administrative Sciences, 25(113), 462-474. https://doi.org/10.33095/jeas.v25i113.1707
