پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 163 |
| تعداد شماره‌ها | 6,762 |
| تعداد مقالات | 72,837 |
| تعداد مشاهده مقاله | 131,857,320 |
| تعداد دریافت فایل اصل مقاله | 103,533,630 |
Relative performance of statistical and fuzzy regression models in estimation of gasoline demand in Iran | ||
| Industrial Management Journal | ||
| مقاله 2، دوره 7، شماره 1، تیر 2015، صفحه 1-19 اصل مقاله (1.2 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/imj.2015.50684 | ||
| نویسندگان | ||
| Mohammad Reza TaghizadehYazdi* 1؛ Hossein Mirshojaeian Hosseini2؛ Ezzatollah Asgharizadeh3؛ Hamed Shakouri Ganjavi3 | ||
| 1Assistant Prof., Industrial Management, Faculty of Management, University of Tehran, Iran | ||
| 2Assistant Prof., Interdisciplinary Economics, Faculty of Economics, University of Tehran, Tehran, Iran | ||
| 3Associate Prof., Industrial Management, Faculty of Management, University of Tehran, Iran | ||
| چکیده | ||
| Gasoline is the most important energy product in the passenger transportation sector in Iran. Gasoline demand survey has a high priority in Iran because of its ever-increasing consumption. The most important challenges for this purpose are uncertainties resulting from structural failure of economy, changing of policies and lack of accurate data. This research aims to specify the variables explaining the gasoline demand in Iran and to estimate gasoline demand by using the statistical and fuzzy regression models over the period 1981-2007. Finally, the models were compared using standard criteria. The results indicate that both methods have enough accuracy for estimating and forecasting gasoline demand. In addition, there is a significant relationship between gasoline price, car per capita and trend as explanatory variables of gasoline demand per capita. | ||
| کلیدواژه‌ها | ||
| fuzzy regression؛ Gasoline Demand؛ Iran؛ statistical regression | ||
| مراجع | ||
|
Azadeh, A., Arab, R. & Behfard, S. (2010). An adaptive intelligent algorithm for forecasting long term gasoline demand estimation: The cases of USA, Canada, Japan, Kuwait and Iran. Expert Systems with Applications, 37: 7427–7437. Chitnis, M. (2005). Estimating price elasticity of gasoline demand using structural time series models and concept of implicit trend. Economics Research Quarterly, 5(3): 1-16. (In Persian)
Crotte, A., Noland, R. B. & Graham, D. J. (2010). An analysis of gasoline demand elasticities at the national and local levels in Mexico. Energy Policy, 38: 4445–4456.
Eltony, M. N. & Al-Mutairi, N. H. (1995). Demand for gasoline in Kuwait, Energy Economics. Energy Economics, 17(3): 249-253.
Gojorati, D. (2010). Principles of Econometrics, Abrishami, H., University of Tehran Press. Tehran. (In Persian)
Khataei, M. & Eghdami, P. (2005). Analysis of price elasticity of gasoline demand in Iran Land transportation and predicting it by the year 1394. Iran Economics Researches, 7(25): 23-46. (In Persian)
Ministry of power (2009). Energy balance sheet 1387, Deputy of Energy. Tehran. (In Persian)
Montgomery, D. C. & Peck, E. A. (1982). Introduction to Linear Regression Analysis, Wiley. New York.
Norozi, H. & Salgi, M. (2006). Surveying Effect of different price scenarios on gasoline. Energy Economics Studies Quarterly. 3(11): 64-83. (In Persian)
Park, S .Y. & Zhao, G. (2010). An estimation of U.S gasoline demand: A smooth time-varying cointegration approach. Energy Economics, 32: 110–120.
Sadeghi, H., Zolfaghari, M. & Heidarzadeh, M. (2009). Estimation of gasoline demand function in transportation sector using genetic algorithm. Energy Economics Studies Quarterly. 6(21): 1-27. (In Persian)
Savic, D. A., Pedrycz, W. (1991). Evaluation of fuzzy linear regression models. Fuzzy Sets and Systems, 39(1): 51-63.
Sene, M. N. & Al-Mutairi, N. H. (2011). Estimating the demand for gasoline in developing countries: Senegal. Energy Economics, In Press.
Shakeri, A., Mohammadi, T., Jahangard, A. & Moosavi, M. (1389). Estimating structural model of demand for gasoline and gas oil in Iran transport sector. Energy Economics Studies Quarterly. 7(25): 1-31. (In Persian)
Stern, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World, McGraw-Hill.Irwin.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning I. Inform. Sci., 8: 199-249.
Zaranejad, M. & Ghapanchi, F. (2007). Estimating Error correction model for gasoline demand in Iran. Business Research Quarterly, 42: 29-52. (In Persian) | ||
|
آمار تعداد مشاهده مقاله: 3,732 تعداد دریافت فایل اصل مقاله: 2,103 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب