| تعداد نشریات | 24 |
| تعداد شمارهها | 851 |
| تعداد مقالات | 7,575 |
| تعداد مشاهده مقاله | 13,413,791 |
| تعداد دریافت فایل اصل مقاله | 11,580,473 |
A comparison of genetic algorithm and auto -regressive distributed lag model in determination of total factors productivity growth in the agricultural sector of iran | ||
| Iran Agricultural Research | ||
| مقاله 12، دوره 34، شماره 2، اسفند 2015، صفحه 101-107 اصل مقاله (368.73 K) | ||
| نوع مقاله: Full Article | ||
| شناسه دیجیتال (DOI): 10.22099/iar.2016.3525 | ||
| نویسندگان | ||
| Samaneh Negarchi* 1؛ M.R. Zare Mehrjerdi1؛ H. Mehrabi Boshrabadi1؛ H. Nezamabadi Pour2 | ||
| 1Department of Agricultural Economics, ShahidBahonar University of Kerman, Kerman, I. R. Iran. | ||
| 2Department of Electrical Engineering, ShahidBahonar University of Kerman, Kerman, I. R. Iran. | ||
| چکیده | ||
| ABSTRACT-Due to the important role productivity plays in future decision making and programming, the productivity indexes should have accurate quantities. In this study, Auto-Regressive Distributed Lag (ARDL) and Genetic Algorithm (GA) methods are applied to time series of 1978-2008 to accurately measure total factor productivity (TFP) in the agricultural sector of Iran. The comparison of these two methods shows that GA method is more efficient than ARDL model. Also, the growth of TFP in the agricultural sector of Iran has had high fluctuations and annual average of productivity growth in this sector has been -0.16 during the period of the study. Therefore, it is necessary to emphasize the optimum use of available inputs, their appropriate combinations and increasing productivity in the agricultural sector of Iran. | ||
| کلیدواژهها | ||
| Genetic Algorithm؛ Auto-Regressive Distributed Lag Total Factors Productivity Agriculture؛ Iran | ||
| مراجع | ||
|
Atkins, F.J., & Coe, P.J. (2002). An ARDL bounds test of the long-run Fisher effect in the United States and Canada, Journal of Macroeconomics, 24, 255–266.
Amjadi, M.H., Nezamabadi Pour, H., & Farsangi, M.M. (2010).Estimation of Electricity Demand of Iran Using Two Heuristic Algorithms.Energy Conversion and Management, 51, 493-497.
Central bank of Islamic Republic of Iran, Report and statistics; 2007 [in Pershian].
Ceylan, H., & Ozturk, H.K. (2004). Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Converse Management, 45, 2525–37.
Chen, P.C., YU, M.M., Chang, C.C., & HSU, S.H. (2008). Total factor productivity growth in China's agricultural sector, China Economic Review, 19, 580–593.
Energy balance-sheet: power ministry of Iran, Energy report and statistics; 2006 [in Persian].
Goldberg, D.E. (1989). Genetic Algorithm in Search, Optimization and Machine Learning.Addison-Wesley.
Haldenbilen, S., & Ceylan, H. (2005). Genetic algorithm approach to estimate transport energy demand in Turkey, Fuel and Energy, 46, 193-204.
Hamamoto, M. (2006).Environmental regulation and the productivity of Japanese manufacturing industries.Journal of Resource and Energy Economics, 604, 14-25.
Holland, J.H. (1992). Adaptation in natural and artificial systems. Cambridge, MA: MIT Press. (First edition, 1975, University of Michigan Press.)
Ozturk, H.K., Ceylan, H., Canyurt, O.E., & Hepbasli, A. (2005). Electricity estimation using genetic algorithm approach: a case study of Turkey. Energy, 30, 1003–12.
Pirasteh, H. (2003). The contribution of agriculture to economic and productivity growth of Iranian economy. Journal Iranian Economic Review, University of Tehran, Faculty of Economic, 8, 45-72.
Pesaran, M.H., Shin, Y., & Smith, R.J. (2001).Bounds testing approaches to the analysis of level relationships.Journal Apply Econometr, 16, 289–326.
Pesaran, H.M., & Shin, Y. (1999). Autoregressive distributed lag modeling approach to cointegration analysis. In: Storm S, editor. Econometrics and economic theory in the 20th century: the Ranger Frisch centennial symposium. Cambridge University Press; [chapter 1].
Romer, D. (2001).Advanced Macroeconomics. Shanghai University of Finance & Economics Press, 5–17.
Simmons, P., & Cacho, O. (1999). a genetic algorithm approach to farm investment.theAustralian Journal of Agricultural and Resource Economics, 43,3,305-322.
Sethuram, S., Girmay, M., Steven, K., Jeffrey, B., & Lant Christopher.(2008). An agent based model of multifunctional agricultural landscape using genetic algorithms. The American agricultural economics association annual meeting, Orlando, fl, July 27-29.
Solow, R. (1956). A Contribution to the Theory of Economic Growth.Quarterly Journal of Economics, 70, 65-94. | ||
|
آمار تعداد مشاهده مقاله: 1,815 تعداد دریافت فایل اصل مقاله: 1,501 |
||