Parameter estimation of a composite production function model based on improved artificial fish swarm algorithm and model application. Issue 10 (26th November 2017)

• Record Type: Journal Article
• Authors: Cheng, Maolin; Xiang, Minyin
• Is Part Of: Communications in statistics. Volume 46:Issue 10(2017)
• Page Start: 8218
• Page End: 8232
• Publication Date: 2017-11-26
• Subjects: Artificial fish swarm algorithm -- Empirical analysis -- Composite production function -- Contribution rate; 62F99 -- 62P20; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2016.1271893
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: The traditional Cobb–Douglas production function uses the compact mathematical form to describe the relationship between the production results and production factors in the production technology process. However, in macro-economic production, multi-structured production exists universally. In order to better demonstrate such input–output relation, a composite production function model is proposed in this article. In aspect of model parameter estimation, artificial fish swarm algorithm is applied. The algorithm has satisfactory performance in overcoming local extreme value and acquiring global extreme value. Moreover, realization of the algorithm does not need the gradient value of the objective function. For this reason, it is adaptive to searching space. Through the improved artificial fish swarm algorithm, convergence rate and precision are both considerably improved. In aspect of model application, the composite production function model is mainly used to calculate economic growth factor contribution rate. In this article, a relatively more accurate calculating method is proposed. In the end, empirical analysis on economic growth contribution rate of China is implemented.