Identifying the Factors Affecting the Recession in Iran: Monte Carlo Simulation and Metropolis-Hastings (MH) Algorithm

Refaei Refaei (), Morteza Sameti () and Sara Ghobadi ()
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Sara Ghobadi: Assistant Professor of Economics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

Quarterly Journal of Applied Theories of Economics, 2019, vol. 6, issue 3, 241-272

Abstract: Changes in macroeconomic indices and variables such as the decline in GDP, the decline in oil exports, and the exchange rate fluctuation indicate a period of recession in the Iranian economy. In this paper, the Monte Carlo Markov Chain (MCMC) and the MH algorithm are used to identify the factors that contributed to this recession during the years 0-1. The studies show that the results of MH algorithm confirm the model estimation results using Monte Carlo Markov chain approach and at 95% confidence level, the coefficients of the variables are statistically significant and reliable. Therefore, the most influential variables on the recession were estimated by Monte Carlo approach, exchange rate changes, crude oil prices, and government corruption. The results also show that the Bayes factor matrix for all estimation models is well-reasoned. The later probabilities of regimes and the final exponential ratio show that the change points in the sixth pattern (with variables: exchange rates, crude oil prices, government corruption and productivity) are different from the rest of the models presented, so regime change occurs in this model.

Keywords: MH Algorithm; Monte Carlo Markov Chain; Recession; Bayes Factors. (search for similar items in EconPapers)
JEL-codes: C21 C63 E32 E37 (search for similar items in EconPapers)
Date: 2019
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