期刊名称：Topics in Middle Eastern and North African Economies
出版年度：2010
卷号：12
期号：1
出版社：Loyola University Chicago
摘要：Economic convergence is one of the important topics of new macroeconomics. It refersto tendency of income per capita of countries (regions) to converge to their steady-statevalue. There are two kinds of convergence: conditional and absolute convergence. Thispaper examines income convergence between 22 MENA countries during the period of1970-2003 by using the neoclassical growth model of Barro-Salla-i-Martin for both kindsof convergence. Non-linearity of the underlying relationships, the restrictiveness ofassumptions of functional forms and econometric problems in the estimation andapplication of theoretical models advocate for the use of Artificial Neural Networks(ANN) algorithms. We show that by changing the quantitative tools of analysis andusing ANN, the results become more precise. Results show that absolute convergenceand conditional convergence are significant but the rate of convergence is low.
关键词：Income Convergence; MENA Countries; Artificial Neural Networks
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