Predicting the dynamics of private sector investment in Iran

Despite significant capacities and potentials, Iran has performed differently in attracting investors and economic actors in recent decades, and if this situation continues, we will see more economic distance with other developed countries in the future. After examining the economic factors affecting private investment in the Iranian economy, using a new and efficient method called DMA, to predict the factors affecting investment in order to accurately identify how the investment responds to changes in these variables during the years 1397-1380. Evaluate and determine which variable (or variables) has been the most influential factor on private sector investment at any point in time in order to provide appropriate policy recommendations to guide economic variables to increase private investment. The results show that the presence of the inflation variable after 2009 is less than 0.5, the probability of the presence of the liquidity variable in 1996 and 1997 is much higher than other years, the probability of the presence of the variable government expenditure from 1389 onwards is high, the GDP variable in Most years are present with high probability in investment forecasting. With the realization of the exchange rate in the years 92-97, the presence of the exchange rate variable is more probable. The probability of the presence of the variable of bank facilities from 1390 onwards is higher. Business is very low in the years under review.The calculations show that in each of the time horizons and considering the values of the transfer matrix, which indicates the probability of the presence of each of the research variables in each time period, the variables present in the best investment forecasting model can change in each period. . In addition, the probability of the presence of each of the variables, which can be calculated based on the presented figures, has different values in each section. This study, in which investment is predicted using a dynamic method and shows how much each variable in each section describes the investment values, shows the nonlinear relationships between the model variables.If these models have acceptable values of good fit indices in different modes, this model is suitable for predicting the desired variable.