Simulation of the Effect of Factors Affecting on Recession in Iran: Comparison of Markov Chain Monte Carlo and Bayesian Approaches

The history of Iran's economy after the revolution has been in recession for some years and, with the 70s, this trend has deepened, and with the 1990s it seems that the real GDP trend is making serious changes. In this paper, the Markov chain Monte Carlo and Byesian approach are used to simulate the effects of factors affecting the economic recession in Iran during the years 1979- 2016. The results show that the Bayesian approach confirm the results of the model estimation using the Monte Carlo Markov chain approach, and at a reliable level, 97.5% of the coefficients of the variables are statistically significant and reliable. so, the most influential variables were estimated on the economic recession in Iran, exchange rate changes, crude oil prices, and real GDP. The results also show that the matrix of Bayes factors for all pairings of models is reliable. The later probabilities of regimes and the likelihood ratio indicate that the change points in the sixth model are different with the rest of the models, so the regime change is happening in the sixth model.