چکیده:
lectricity cannot be stored and needs huge amount of capital so producers and consumers pay special attention to predict electricity consumption. Besides, time series data of the electricity market are chaotic and complicated. Nonlinear methods such as Neural Networks have shown better performance for predicting such kind of data. We also need to analyze other variables affecting electricity consumption so as to estimate their quantitative effects. This paper presents a new approach for forecasting: a combined method of Neural Networks (ANN) and econometrics methods which can also explain the effect of raising the electricity prices on consumption after Subsidies Reform Plan. Data is from 1988-2008, and the method is compared with Neural Network and ARIMA based on the RMSE performance function that shows the advantage of the combinned approach. The provident prediction is done for 2009- 2014 and indicated that after decreasing subsidy, electricity consumption would increase slightly until 2014.
خلاصه ماشینی:
"2- Empirical Literature Since the pioneered work of Sims (1980, 1992) and Bernanke and Blinder (1992), the VAR models has become the standard methodology used in the analysis of monetary policy shocks and in measuring their effects upon macroeconomic variables.
Peersman and Smets (2001) measure the macroeconomic effects of an unanticipated change in monetary policy in Euro area by using VAR model and concluded that a rise in the short term nominal interest rate leads to a real appreciation of the exchange rate with a fall in output, while prices shows sluggish behavior and fall significantly after several quarters.
He finds that a contractionary monetary policy leads to a hump shaped pattern of GDP which is consistent with theory and the IRF obtained from FAVAR model are in line with the literature and make sense from an economic point of view, while comparing the results of FAVAR with VAR models, finds that the inclusion of the information captured by the factors mitigates the price puzzle.
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