The Application of Artificial Neural Networks in Managing Country’s Major Financial and Macroeconomic Indicators: Forecasting Gini Coefficient of Household Income Inequality and Urban-Rural Income Ratio in China

This paper represents a robust and applicable Artificial Neural Network to forecast Gini coefficient of household income inequality and urban-rural income ratio considered as dependent variables using a number of financial, economic, and human indicators as their determinative variables based on the data observed from 1991 to 2009 in China, with no need for intricate calculations. For this purpose, an Artificial Neural Network was initially constructed and repeatedly trained to acquire the best output approximated to target data observed during 1991-2007. Afterwards, the robustness of the best-trained Artificial Neural Network was evaluated and approved by introducing the data related to 2008 -2009 as new data and subsequently comparing new outputs with observed target data in the same period to indicate that such an Artificial Neural Network can be reliably applied to different samples of data to forecast the aforementioned dependent variables in managing country’s major financial and macroeconomic indicators.

کلیدواژه ها:

Artificial Neural Network ، Forecasting ، Major Financial and Macroeconomic Indicators ، Household Income Inequality ، Gini Coefficient ، Urban-Rural Income Ratio