Forecast of Total Factor Productivity in the Economy

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DIZAJI MONIREH

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Journal: ECONOMIC STRATEGY Year:2018 | Volume:7 | Issue:25 Page(s): 45-70

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Title

Forecast of Total Factor Productivity in the Economy

Author(s)

DIZAJI MONIREH | Issue Writer Certificate

Keywords

ForecastingQ2

total factor productivityQ2

neural networksQ1

Abstract

The study has been done to predict the total factor productivity in Iranian economy in 1996 to 2016 years. In this study, the feed forward neural network whit back propagation algorithm used to predict. 5 different configurations of inputs are used to design 5 models and 30 different scenarios in each model on the number neurons, designed and implemented to predict. Best models of neurons and networks of neurons fourth model with 19 active functions TANSIG input and output function is LOGSIG and by correlation coefficient (R) Mean Square Error (MSE) and root mean square error (RMSE) and normalized root mean square error (RMSE N), a Best model has been selected and they are equal to 0. 9985, 0. 0111, 0. 1055 and 2. 62, respectively. This indicates that the artificial neural networks were designed in this study have the ability to predict the Iranian economy's total factor productivity.

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APA: Copy

DIZAJI, MONIREH. (2018). Forecast of Total Factor Productivity in the Economy. ECONOMIC STRATEGY, 7(25 ), 45-70. SID. https://sid.ir/paper/233829/en

Vancouver: Copy

DIZAJI MONIREH. Forecast of Total Factor Productivity in the Economy. ECONOMIC STRATEGY[Internet]. 2018;7(25 ):45-70. Available from: https://sid.ir/paper/233829/en

IEEE: Copy

MONIREH DIZAJI, “Forecast of Total Factor Productivity in the Economy,” ECONOMIC STRATEGY, vol. 7, no. 25 , pp. 45–70, 2018, [Online]. Available: https://sid.ir/paper/233829/en

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