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 IN ATION IN IRAN

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Pendar M.; HAJI M.

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Paper Information

Journal: INTERNATIONAL JOURNAL OF INDUSTRIAL MATHEMATICS Year:2017 | Volume:9 | Issue:2 Page(s): 119-128

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Title

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 IN ATION IN IRAN

Author(s)

Pendar M. | HAJI M. | Issue Writer Certificate

Keywords

INATIONQ2

FORECASTQ2

NEURAL NETWORKQ2

VECTOR AUTO REGRESSIONQ2

BAYESIAN VECTOR- AUTOREGRESSIVEQ2

GARCHQ2

TIME SERIESQ2

Abstract

This paper has two aims. The first is FORECASTing INATION in Iran using Macroeconomic variables data in Iran (INATION 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 INATION is FORECASTed. The comparison of performance of FORECASTing models used to FORECAST Iran's INATION 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 INATION, liquidity, GDP, prices of imported goods and exchange rates at free market to estimate di erent models in this paper and compare root mean square error and Mean Absolute Percentage Error of models by which INATION has been FORECASTed, NEURAL NETWORK model had better performance than others models in FORECASTing Iran' s INATION. 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.

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APA: Copy

Pendar, M., & HAJI, M.. (2017). 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 IN ATION IN IRAN. INTERNATIONAL JOURNAL OF INDUSTRIAL MATHEMATICS, 9(2), 119-128. SID. https://sid.ir/paper/231797/en

Vancouver: Copy

Pendar M., HAJI M.. 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 IN ATION IN IRAN. INTERNATIONAL JOURNAL OF INDUSTRIAL MATHEMATICS[Internet]. 2017;9(2):119-128. Available from: https://sid.ir/paper/231797/en

IEEE: Copy

M. Pendar, and M. HAJI, “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 IN ATION IN IRAN,” INTERNATIONAL JOURNAL OF INDUSTRIAL MATHEMATICS, vol. 9, no. 2, pp. 119–128, 2017, [Online]. Available: https://sid.ir/paper/231797/en

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