Optimization and Simulation (Monte Carlo) of the Impact of Productivity Shocks on GDP of Iran using the Advanced Algorithms Approach

Document Type : Research Paper

Authors

1 Ph.D. Candidate in Economy, Islamic Azad University, Kerman, Iran

2 Assistant Prof. of Economy, Islamic Azad University, Kerman, Iran

3 Prof. of Economy, Shahid Bahonar University, Kerman, Iran

Abstract

In this paper, the impact of productivity shocks on GDP using advanced algorithm approach and the Monte Carlo simulation in the Iranian economy has been surveyed. After reviewing the theoretical and experimental studies, the variables of inflation, unemployment, potential production and productivity shocks are selected as the variables explaining the variable of gross domestic product. Using three algorithms of fireflies, cuckoo, and particle swarms optimization, the coefficients of each of the independent variables were estimated. After estimating the coefficients and given the uncertainty of the estimated coefficients by the advanced algorithms, Monte Carlo method was used to simulate the equations. Comparing the findings obtained from the simulation and findings of the estimation indicate the high accuracy of the findings obtained from estimates. Given the obtained findings, productivity shocks had very little impact on GDP and the potential production was introduced as the most influential variable.

Keywords


Abbasian, A., Mehregan, M. (2007). Measurement of productivity of production factors of the country's economic sectors by data envelopment analysis. Economic Research Journal, (87), 153-176. (in Persian)
Amir Teymuri, S., (2016). The role of the educated labor force in the growth of the productivity of total production factors in Iran's agricultural sector. Agriculture Management Management Research Quarterly, 8 (36), 55-63.
(in Persian)
Bayat, F. (2014). Superstructural optimization algorithms (along with applications in electrical engineering). Jahad University Press, Tehran. (in Persian)
Eberhart, J., Kennedy, R. (1995). Particle Swarm Optimization. InProceedingsIEEEInternationalConferenceonNeuralNetworks.
Fallahi, A., Hosseinzadeh, M., Moghaddam Nejad, H. (2012). Investigating the relationship between productivity and employment changes in Iranian industry. Economic Growth and Development Research, 2 (8), 23-36.
(in Persian)
Faulkner, D. & Makrelov, K. (2009). Productivity-raisinginterventionsfortheSouthAfricaneconomy:acgeanalysis. The ecomod, university of Ottawa.
Fetros, M., Dehghanpour, M. (2011). Effect of Productivity on Economic Growth of Iranian Manufacturing Industries by Combined Data Approach. Development Management Process, 25 (1), 27- 44. (in Persian)
Han, G., Kalirajan, K. & Singh, R. (2003). “Santa”. Efficiency and Economic Growth: East Asia and the Aest of the world 1005. Center for Cruz Center for International Economic. Working paper Series International Economics, UC Santa Cruz.
Jajri, I. (2011). Total Factor Productivity and Output Growth in Malaysian. Research Journal of Applied Sciences, 5(1), 63-76.
Kinyondo, G. & Mabugu, M. (2008). The general equilibrium effects of a productivity increase on the economy and gender in south africa. SouthAfricanjournalofEconomicandManagement, 12(3), 307-326.
Liang, C. (2001), Measuring Total Factor Productivity in Republic of China, Measuring Total Factor Productivity, Tokyo: Asian Productivity Organization, 26(3), 15-29.
Rajabi, F. (2014). Study of the relationship between productivity, inflation and production: A case study of Iran agricultural sector. International Green Economy Online Conference. Babolsar, (May 22). (in Persian)
Rajabioun, R. (2011). Cuckoo optimization algorithm. Appliedsoftcomputing, 11(8), 508- 518.
Rezaei, J., Nadali, M., Alizadeh, J. (2010). Investigating the relationship between the growth of total factor productivity and economic growth. Economic Research, 11 (2), 111-135. (in Persian)
Robert, P.C. & Casella, G. (2004). MonteCarlostatisticalmethods. 2nd edG. Springer-verlag, New York.
Sahay, B.S. (2005). Multi-factor productivity measurement model for service organization. InternationalJournalofProductivityandPerformanceManagement, 54(1), 7-22.
Scherngell, T., Borowiecki, M. & Hu, Y. (2014). Effects of knowledge capital on total factor productivity in China: A spatial econometric perspective. ChinaEconomicReview, 29, 82-94.
Shakeri, A.S. (2008). Theories and macroeconomic policies. Pars Newspaper Publishing, Tehran. (in Persian)
Sinclair, T. (2004). PermanentandTransitoryMovementsinOutputandUnemployment:Okun’sLawPersists. Manuscript, Washington University in st. Louis.
Tobutt, D. (1982). Monte Carlo simulation methods for slope stability. Computers&Geosciences, 8(2), 199-208.
Vali Jani, B. (2015). Investigating the Factors Affecting the Promotion of Human Resources Productivity in the Organization of Tax Affairs of the Country. Tax Committee, 77 (29), 165-184. (in Persian)
Valizadeh Zenuz, P. (2005). A Study of Productivity in Iran's Economy. The Economic Research Center of the Central Bank of the Islamic Republic of Iran, 24th. (in Persian)
Yang, X.-S., (2010). Firefly algorithm, stochastic test functions and design optimization. InternationalJournalofBio-inspiredComputation, 2(2), 78-84.