Mojtaba Kazemi; Seyed Abdolmajid Jalaee Esfand Abadi; Hossein Akbari Fard
Volume 4, Issue 15 , August 2014, , Pages 40-25
Abstract
In this paper, in order to empirically examine and predict the effect of exchange rate uncertainty on economic growth in Iran over the period 1959 to 2010, econometrics methods and artificial neural network are applied. For this purpose, at first the exchange rate uncertainty is calculated by the generalized ...
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In this paper, in order to empirically examine and predict the effect of exchange rate uncertainty on economic growth in Iran over the period 1959 to 2010, econometrics methods and artificial neural network are applied. For this purpose, at first the exchange rate uncertainty is calculated by the generalized autoregressive conditional heteroskedasticity (GARCH) method. Then the impact of exchange rate uncertainty on economic growth in Iran has been tested. For this purpose, the proper network, in according to valuation criterions like determination coefficient and mean square of error were determined. Then research hypothesis has been investigated by attention to trained artificial neural network. The results indicate that exchange rate uncertainty has had a weak negative effect on Iran economic growth in recent years. Of course, it is expected that this effect in the future to be significantly stronger.
Seyyed Abdolmajid Jalayee; Omid sattari
Volume 1, Issue 4 , December 2012, , Pages 144-117
Abstract
Globalization, the process of considerable increase in international trade, global exchanges and markets’ integration as a fundamental characteristic, are emerging inevitably. Investigating the way in which this process affects economic variables, can be a guidance of decision making for policy ...
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Globalization, the process of considerable increase in international trade, global exchanges and markets’ integration as a fundamental characteristic, are emerging inevitably. Investigating the way in which this process affects economic variables, can be a guidance of decision making for policy makers. Considering structural economic differences between urban and rural societies in Iran and using 1350-1386 Iran’s economy dataset, first we compared the efficiency of VAR and VEC models with artificial neural network (ANN) approach in forecasting measure of income distribution inequality of urban societies and finally the best model (ANN) has used as an out-of-sample forecasting tool in different designed scenarios from 1387 to 1395. Choosing ANN model, decrease in urban societies’ income inequality during globalization process, is the main result.
