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This paper empirically examines the determinants of sovereign credit ratings using panel data on a sample of 40 countries for the period 1997-2016. An out-of-sample forecast of ratings is provided and the accuracy of models is compared. In addition, a model for prediction of probability of sovereign credit rating downgrade is proposed in this research. The estimation results reveal that different variables from previous period such as GDP per capita, GDP growth, unemployment, inflation, government debt and government effectiveness affect sovereign credit ratings. Also it is found that previous period GDP per capita, GDP growth, current account, fiscal balance and government effectiveness influence probability of downgrade of sovereign credit rating. As for forecasting results, 70% of forecasted ratings based on ordered probit model had one-notch error or zero error, while 90% of results had two-notch error or lower. The accuracy for linear model occurred to be lower and it was concluded that it is better to use non-linear model for forecasting. The main contribution of this paper is that minimum credit rating for the year is used as dependent variable and lagged explanatory variables are employed. Consequently, results of this research may be useful for prediction purposes and lead to greater understanding of factors that affect sovereign credit ratings.

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