Modeling and Estimation of Uncertainty Time Series of Iran’s Private and Public Investments Using Vasicek’s Mean Reverting Stochastic Differential Equation (Period 1340-1400)

Document Type : Research Article

Authors

1 Ph.D. Student in Economics-Economic development and planning, Department of Economics, Faculty of Social Sciences, Razi Universit, Kermanshah, Iran.

2 Associate Professor, Department of Economics, Faculty of Social Sciences, Razi University, Kermanshah, Iran

Abstract

Investigating the investment trend of the past decade in the country shows that the average growth of investment in those years has been decreasing. Factors such as the high attractiveness of the country’s non-productive markets, the high cost of the business environment, currency fluctuations, and the problems caused by the embargo, etc, have led to a decrease in investment and created a volatile environment with uncertainties in this variable. Due to the importance of investment in the macro economy, unfavorable conditions of the country and resulting insecurities will lead to a decrease in investment and economic growth. Therefore, this research aims to simulate the uncertainty variables of private and government investment due to the base years of 1997 and 2011, from 1961 to 2021. we performed This simulation using the Vasichek mean reversion stochastic differential equations method, which has an advantage due to the examination of the assumptions of competing models (neural network, ARIMA, etc.) [1, 2]. According to the simulation results, in all the years except 1963, Private investment uncertainties were more than government investment uncertainties. Also, the highest amount of private investment uncertainties are related to 2014, 2015, and 2013, respectively, and the lowest amount is related to 1961, 1963, and 1962, respectively. Meanwhile, the highest amount of government investment uncertainties are related to 1978, 1979, and 1980, respectively, and the lowest amount is related to 1961, 1962, and 1964, respectively.

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Main Subjects


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