مقاله Comparison of Neural Network Models Vector Auto Regression (VAR) Bayesian Vector-Autoregressive (BVAR) Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) Process and Time Series in Forecasting Inflation in Iran
توجه : به همراه فایل word این محصول فایل پاورپوینت (PowerPoint) و اسلاید های آن به صورت هدیه ارائه خواهد شد
مقاله Comparison of Neural Network Models Vector Auto Regression (VAR) Bayesian Vector-Autoregressive (BVAR) Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) Process and Time Series in Forecasting Inflation in Iran دارای ۱۷ صفحه می باشد و دارای تنظیمات در microsoft word می باشد و آماده پرینت یا چاپ است
فایل ورد مقاله Comparison of Neural Network Models Vector Auto Regression (VAR) Bayesian Vector-Autoregressive (BVAR) Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) Process and Time Series in Forecasting Inflation in Iran کاملا فرمت بندی و تنظیم شده در استاندارد دانشگاه و مراکز دولتی می باشد.
توجه : در صورت مشاهده بهم ریختگی احتمالی در متون زیر ،دلیل ان کپی کردن این مطالب از داخل فایل ورد می باشد و در فایل اصلی مقاله Comparison of Neural Network Models Vector Auto Regression (VAR) Bayesian Vector-Autoregressive (BVAR) Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) Process and Time Series in Forecasting Inflation in Iran،به هیچ وجه بهم ریختگی وجود ندارد
بخشی از متن مقاله Comparison of Neural Network Models Vector Auto Regression (VAR) Bayesian Vector-Autoregressive (BVAR) Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) Process and Time Series in Forecasting Inflation in Iran :
سال انتشار : ۲۰۱۷
تعداد صفحات :۱۷
This paper has two aims. The first is forecasting inflation in Iran using Macroeconomic variables data in Iran (Inflation rate, liquidity, GDP, prices of imported goods and exchange rates) , and the second is comparing the performance of forecasting vector auto regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran”s inflation is forecasted. The comparison of performance of forecasting models used to forecast Iran”s inflation has been done based on the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the models. Due to the annual values of Inflation, liquidity, GDP, prices of imported goods and exchange rates at free market to estimate different models in this paper and compare root mean square error and Mean Absolute Percentage Error of models by which inflation has been forecasted, neural network model had better performance than others models in forecasting Iran”s inflation. Indeed root mean square error and Mean Absolute Percentage Error of neural network model have less value rather than root mean square error and Mean Absolute Percentage Error of other forecasting models.
- در صورتی که به هر دلیلی موفق به دانلود فایل مورد نظر نشدید با ما تماس بگیرید.