Identifying and Analyzing the Causal Relationships between the Effective Macroeconomic Variables on Iran's Economic Growth Using Bayesian Casual Map Method

Achievement high economic growth in all societies is main purpose of the policy makers and because of that economists are trying to use different theoretical and empirical methods to analyze and identifying the economic growth and the effective factors on it. Because of oil revenues volatility, high inflation, debt of government to Central Bank and other problems, the mentioned subject is more highlighted in Iran. We used casual relations approaches and Bayesian Casual Map (BCM) method for investigation relationships among macroeconomic variables that have affected on the economic growth in Iran. We investigated the causal relation among variables by using GES algorithm in the Tetrad software and after modifying casual relationships, we determined the variable probabilities by Netica Software. Based on first scenario analysis, increases in oil revenue have a direct and positive effect on economic growth, but tax and debts scenarios indicate that have an direct and negative effect on economic growth. Eventually replace the tax revenues and the sale of saving bonds instead of oil revenues for avoiding continual failures such as debt, deficit, increased borrowing from the central bank to increase liquidity and ultimately inflation was proposed.