application of integrated neural network and input-output models in forecasting total production and final demand

نویسندگان

عبدالرسول قاسمی

استادیار دانشکده ی اقتصاد دانشگاه علامه طباطبایی علی اصغر بانویی

دانشیار دانشکده ی اقتصاد دانشگاه علامه طباطبایی فاطمه آقائی

کارشناسی ارشد دانشکده اقتصاد دانشگاه علامه طباطبایی

چکیده

forecasting of macroeconomic variables has specific importance in economic topics. indeed, different models are invented to forecast variables to help economic policy makers in adopting appropriate monetary and fiscal policies. in this paper, the performance of integrated model of input-output (io) and neural network is investigated in forecasting final demand and total production and the results are compared with io model. at the first step, final demand is estimated by using mean of final demand rates over the period 1365-1375, and then total production is forecasted by using io model. in the next step, two generalized feed forward neural networks are proposed to forecast final demand and total production of the year 1380. finally, two models are compared and the hypothesis is evaluated by using mse, rmse, mad, mape criteria. the results indicate that the integrated model of io and neural network outperform io model in forecasting total production. jel classification: c53, d57, c54

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Using Neural Network to Determine Input Excesses, Output Shortfalls and Efficiency of Dmus in Russell Mode

Data Envelopment Analysis (DEA) has two fundamental approaches for assessing theefficiency with different characteristics; radial and non-radial models. This paper isconcerned the non-radial model of Russell which is a non linear model. Conventional DEAfor a large dataset with many inputs/outputs would require huge computer resources in termsof memory and CPU time. Artificial Neural Network (AN...

متن کامل

Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange

During the recent decades, neural network models have been focused upon by researchers due to their more real performance and on this basis, different types of these models have been used in forecasting. Now, there is a question that which kind of these models has more explanatory power in forecasting the future processes of the stock. In line with this, the present paper made a comparison betw...

متن کامل

supply and demand security of energy in central asia and the caucasus

امنیت انرژی به معنی عرضه مداوم و پایدار همراه با قیمت های معقول در حامل های انرژی، که تهدیدات امنیتی، سیاسی، اقتصادی، محیط زیستی و روانی را کاهش دهد. امروزه نفت و گاز تنها، کالای تجاری نیست بلکه بعنوان ابزار سیاسی مورد استفاده قرار می گیرد.اختلاف میان روسیه و اکراین بر سر انرژی در ژانویه 2006، تهدیدی برای امنیت انرژی اروپا ایجاد کرد. در این تحقیق ما تلاش کردیم که نقش انرژی آسیای میانه و قفقاز ر...

15 صفحه اول

scour modeling piles of kambuzia industrial city bridge using hec-ras and artificial neural network

today, scouring is one of the important topics in the river and coastal engineering so that the most destruction in the bridges is occurred due to this phenomenon. whereas the bridges are assumed as the most important connecting structures in the communications roads in the country and their importance is doubled while floodwater, thus exact design and maintenance thereof is very crucial. f...

forecasting of agricultural crops import in iran artificial neural network and econometric models application

abstract in the present study, agricultural sector import was forecasted by using the econometric and the ann methods. import data from 1971 to 2004 and 2004-2009 was used for forecasting, network training and testing forecast accuracy, respectively. the results shown that feed-forward neural network has much less error and better performance than the arima and the var methods. on the basis of ...

متن کامل

Forecasting Natural Gas Demand Using Meteorological Data: Neural Network Method

The need for prediction and patterns of gas consumption especially in the cold seasons is essential for consumption management and policy planning decision making. In residential and commercial uses which account for the bulk of gas consumption in the country the effects of meteorological variables have the highest impact on consumption.  In the present research four variables include daily ave...

متن کامل

منابع من

با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید


عنوان ژورنال:
تحقیقات اقتصادی

جلد ۴۷، شماره ۴، صفحات ۱۳۷-۱۵۴

کلمات کلیدی
[ ' f o r e c a s t i n g o f m a c r o e c o n o m i c v a r i a b l e s h a s s p e c i f i c i m p o r t a n c e i n e c o n o m i c t o p i c s . i n d e e d ' , ' d i f f e r e n t m o d e l s a r e i n v e n t e d t o f o r e c a s t v a r i a b l e s t o h e l p e c o n o m i c p o l i c y m a k e r s i n a d o p t i n g a p p r o p r i a t e m o n e t a r y a n d f i s c a l p o l i c i e s . i n t h i s p a p e r ' , ' t h e p e r f o r m a n c e o f i n t e g r a t e d m o d e l o f i n p u t ' , ' o u t p u t ( i o ) a n d n e u r a l n e t w o r k i s i n v e s t i g a t e d i n f o r e c a s t i n g f i n a l d e m a n d a n d t o t a l p r o d u c t i o n a n d t h e r e s u l t s a r e c o m p a r e d w i t h i o m o d e l . a t t h e f i r s t s t e p ' , ' f i n a l d e m a n d i s e s t i m a t e d b y u s i n g m e a n o f f i n a l d e m a n d r a t e s o v e r t h e p e r i o d 1 3 6 5 ' , 1 3 7 5 , ' a n d t h e n t o t a l p r o d u c t i o n i s f o r e c a s t e d b y u s i n g i o m o d e l . i n t h e n e x t s t e p ' , ' t w o g e n e r a l i z e d f e e d f o r w a r d n e u r a l n e t w o r k s a r e p r o p o s e d t o f o r e c a s t f i n a l d e m a n d a n d t o t a l p r o d u c t i o n o f t h e y e a r 1 3 8 0 . f i n a l l y ' , ' t w o m o d e l s a r e c o m p a r e d a n d t h e h y p o t h e s i s i s e v a l u a t e d b y u s i n g m s e ' , ' r m s e ' , ' m a d ' , ' m a p e c r i t e r i a . t h e r e s u l t s i n d i c a t e t h a t t h e i n t e g r a t e d m o d e l o f i o a n d n e u r a l n e t w o r k o u t p e r f o r m i o m o d e l i n f o r e c a s t i n g t o t a l p r o d u c t i o n . r n j e l c l a s s i f i c a t i o n : c 5 3 ' , ' d 5 7 ' , ' c 5 4 ' ]

میزبانی شده توسط پلتفرم ابری doprax.com

copyright © 2015-2023