The main purpose of this study is to investigate the relationship and effects of financial cycles on economic growth cycles by using uncertainty in the model. One of the appropriate techniques in the model is the Bayesian Model Averaging.This study consider the strong affecting factors of the financial cycles on the economic growth cycles in Iran during period 2009 to 2018. The studied variables are GDP, Total Index of Tehran Stock Exchange, Average Cost per Square Meter, Govermental and nongovernmental Current Credit, oil Revenues and Government current payments.First, the cycles are extracted by using the Hodrick–Prescott (HP) filter.Then, the relationship between financial cycles and economic growth cycles is investigated using the Granger causality test. The results show bilateral causal relationship between the Variables Exception of current credit with economic growth cycles.Finally, estimation of eight regressions and Bayesian model averaging of the coefficients show that all five explanatory variables have been identified as the most important factors on the economic growth cycles in Iran.Also,we determine the variables of the Stock Exchange Index,current credit and current payments have the positive effect on the economic growth cycles, and the variables of the housing and oil revenues cycles have the negative effect on the economic growth cycles index. The results of this study can be used by macroeconomic planners from a policy viewpoint.