Document Type : Research Paper

Author

Department of Management, Semnan Branch, Islamic Azad University, Semnan, Iran.

Abstract

The prediction of economic variables is one of the main issues in a country's macro decisions. Since in many cases there is no historical data for this purpose and it is necessary to have more than one output, it is necessary to use expert opinions and consequently, model expert opinions in the form of mathematical functions, adds to the complexity of the task and the importance of the problem. To solve such problems, this paper presents a ten-step process using fuzzy rule-based systems. At the first step, the three inputs that include: the price of OPEC oil, the level of Iran and Saudi relations and the level of political tension in OPEC member countries and also the three output variables that include: the amount of employment, the economic growth, and the oil price forecast, have been modeled in the form of trapezoidal and triangular functions. Then, these variables have been converted to linear functions. In the next steps, the three-dimensional decision tables were designed and then by using the fuzzy rule-based systems (if, ... Then...), the preconditions and sequences (results) of the decision rules were written and coded in the Matlab software. The results indicate that the outputs are in line with the existing economic realities of Iran and that three input variables to a certain extent can cause changes in the three output variables. Less technical so far with problems with this complexity of problems are capable of results with this obvious.

Keywords

Sajadi, H., Farazmand, H., & Alisufi, H. (2010). Investigating the relationship between macroeconomic variables and stock index returns in cash: Tehran Stock Exchange. Journal of macroeconomics (journal of economics sciences) 10(2), 123-150.
[2] Mehrara, M., Certain, A., Ahrari, M., & Hamouni, A. (2013). Modeling and predicting the Tehran Stock Exchange Index and determining the effective variables on it. Quarterly journal of economic research and policy, 50.
[3] Golestani, S., Ansari.L., S, & Abbaspour, R. (2014). Oil price forecast with ARFIMA_GARCH and fuzzy logic. The journal of energy economics studies, 10(41), 153-174. (In Persian)
[4] Natarajan, G. S., & Ashok, A. (2018). Multivariate Forecasting of Crude Oil Spot Prices using Neural Networks. arXiv preprint arXiv:1811.08963.
[5] Liu, H., & Chang, Y. (2017). Research on international crude oil price forecasting model. International journal of new development engineering and society, 1(3), 78-81.
[6] Bategeka, L, N., and Matovu, J. M (2011). Oil wealth and potential dutch disease effects in Uganda. Economic policy research centre, 1-36
[7] Luo, Z., Cai, X., Tanaka, K., Takiguchi, T., Kinkyo, T., & Hamori, S. (2019). Can we forecast daily oil futures prices? Experimental evidence from convolutional neural networks. Journal of risk and financial management12(1), 9.
[8] Karimzadeh, S. D., & Honarvar, N. (2017). Investigating the long-run relationship between crude oil price, gold price, housing price index and exchange rate in Iran using a structural vector error correction approach. Journal of energy economics studies, 53, 135-164. (In Persian)
[9] Verharami, V., & Sadeghi, M. (2017). The asymmetric effect of crude oil prices on demand in selected OPEC countries is the price analysis and dynamic panel. Journal of energy economics studies, thirteenth, 52, 35-59. (In Persian)
[10] rezazadeh, a., & Jahangiri, K. (2017). Impact of oil price volatility on the economic growth of major oil-producing countries: vector autogeneration approach in panel Data (PVAR). Journal of energy economics studies, thirteenth, 52, 153-180. (In Persian)
[11] Dindar Rostami, M., Shirinbakhsh, S., & Afshari, Z. (2019). The effects of oil price shocks on discretionary fiscal policy in selected opec countries: panel structural vector autoregressive. Iranian journal of economic studies8(1), 7-25.
[12] Davies, P. (2007). What’s the Value of an Energy Economist?. Speech presented at the International Association for Energy Economics, Wellington, New Zealand.
[13] Baumeister, C., & Kilian, L. (2012). Real-time forecasts of the real price of oil. Journal of business & economic statistics30(2), 326-336.
[14] Baumeister, C., & Kilian, L. (2014). What central bankers need to know about forecasting oil prices? International economic review55(3), 869-889.
[15]  Baumeister, C., Kilian, L and Zhou, X. (2014). Is product spreads useful for forecasting oil prices? an empirical evaluation of the verleger hypothesis, forthcoming, macroeconomic dynamics. Retrieved 20 September, 2019 from https://pdfs.semanticscholar.org/3b9d/1f71973aad8f9724648f214f0625713e07db.pdf
[16] Besso, C. R., & Pamen, E. P. F. (2017). Oil price shock and economic growth. Experience of cemac countries, 8(1).
[17] Aimer, N. M. M. (2016). The effects of fluctuations of oil price on economic growth of Libya. Energy economics letters3(2), 17-29.
[18] Ftiti, Z., Guesmi, K., Teulon, F., & Chouachi, S. (2016). Relationship between crude oil prices and economic growth in selected OPEC countries. Journal of applied business research32(1), 11.
[19] Nicholis, S. C., & Sumpter, D. J. T. (2011). A dynamical approach to stock market fluctuation. International journal of bifurcation and chaos21(12), 3557-564.
 [20] Olaniyi, A. A., Adedotun, K. O., & Samuel, O. A. (2018). Forecasting methods for domestic air passenger demand in Nigeria. Journal of applied research on industrial engineering, 5(2), 146-155.
[21] Lee, Y. H., Hu, H. N., & Chiou, J. S. (2010). Jump dynamics with structural breaks for crude oil prices. Energy economics32(2), 343-350.
[23] Li, T., Hu, Z., Jia, Y., Wu, J., & Zhou, Y. (2018). Forecasting crude oil prices using ensemble empirical mode decomposition and sparse Bayesian learning. Energies11(7), 1882.
[24] Rasoli, S., Tabesh, H., & Etminani, K. (2018). Evaluation of artificial intelligence models of time series in forecasting the number of hospital inpatient admission. Journal of health and biomedical informations, 5(1), 12-24. (In Persian)
 [25]Mohaghegh, S., Richardson, M., & Ameri, S. (2001). Use of intelligent systems in reservoir characterization via synthetic magnetic resonance logs. Journal of petroleum science and engineering29(3-4), 189-204.
[26] Preeti, G., & Santi, B (2012). Stock market forecasting techniques: A survey. Journal of theoretical and applied information technology. 1(46). a24-30
[27] Weiss, W. W., Balch, R. S., & Stubbs, B. A. (2002, January). How artificial intelligence methods can forecast oil production. In SPE/DOE improved oil recovery symposium. Society of Petroleum Engineers.
[28] Keerthan, J, S. Nagasi, Y. & Shaik, S. (2019). Machine learning algorithms for oil in price prediction. International journal of innovative technology and exploring engineering (IJITEE), 8(8), 958.963.
[29] Dourra, H., & Siy, P. (2002). Investment using technical analysis and fuzzy logic. Fuzzy sets and systems127(2), 221-240.
[30] Shirouehzad, H., & Anvari, S. M. (2014). Prioritization of sustainable production indicators using fuzzy inference system. Journal of applied research on industrial engineering (JARIE), 1(2), 96-111.
[31] Washington. D. C. (2017). What Drives Crude Oil Prices. Retrieved 20 September, 2019 from https://knoema.com/WHTDRCOP2019Oct/what-drives-crude-oil-prices-october-2019
 [32] Hammond, J. L. (2011). The resource curse and oil revenues in Angola and Venezuela. Science & society75(3), 348-378.
[33] Mehrara, M., & Makini, N, M. (2009). Investigation of the nonlinear relationship between oil revenues and economic growth using the limit method (Case of Iran). Journal of energy economics, 6(22), 29-52. (In Persian)
[34] Chien, C. (1990). Fuzzy logic in control systems: fuzzy logic controller. IEEE Trans Syst Man Cybern Part II20, 429-434.
[35] Kerre, E. E. (1992, August). A comparative study of the behavior of some popular fuzzy implication operators on the generalized modus ponens. In Fuzzy logic for the management of uncertainty (pp. 281-295). John Wiley & Sons, Inc.
[36] Cao, Z., & Kandel, A. (1989). Applicability of some fuzzy implication operators. Fuzzy sets and systems31(2), 151-186.