This paper focuses on developing models to study influential factors on the INFLATION rate for a panel of available countries in the World Bank data base during 2008-2012. For this purpose, Random effect log-linear and Ordinal logistic models are used for the analysis of continuous and categorical INFLATION rate variables. As the original INFLATION rate response to variables shows an apparent right skewness, the log transformation in the linear mixed effect model seems necessery. In the ordinal logistic mixed effect model, as a new approach, the INFLATION rate variable is categorized based on two threshols to increase model predictibality and precision. These two models consider the potential serial correlation between annual infltion rates and categories through introducing some latent random effect parameters. The results of both models show that money growth, GDP, oil price and income levels of the available countries are significant predictors with increasing effect on the next year INFLATION rate category. Using the categorical INFLATION response variable yields some superior results where government expenditure, exchange rate and capital formation are also detected as significant determinants of ordinal INFLATION variable. Also, the random effect variance is highly significant in both models which shows the necessery need for consideration of the potential association of INFLATION variables across time.