This paper investigates variations in the accuracy of forecasting key macroeconomic indicators through the comparison of Frequentist and Bayesian vector autoregression (VAR) models. The primary aim of the study is to identify the most effective prior type in minimizing forecast errors for the key macroeconomic indicators in the context of the Russian economy. A significant aspect of this research involves elucidating the theoretical foundations of Bayesian methods and delineating the roles of different priors in the prediction of macroeconomic indicators. A pivotal consideration in the application of the Bayesian approach is the diversity of priors, such as Jeffreys and Minnesota, which may overlook economic considerations like inflation targeting and neutral money. Conversely, certain priors, such as steady-state or independent normal-Wishart priors, are grounded in economic policy. The study delves into the nuanced interplay between these priors and their implications for forecasting accuracy. The empirical findings reveal that all Bayesian VARs exhibit superior forecasting accuracy compared to the classical VARs. Furthermore, expanding the model's scope from a limited number of variables (4) to a more comprehensive set (18) enhances forecast precision, as evidenced by the escalating log-predictive scores, Model Confidence Sets, and The Diebold-Mariano test. Simultaneously, the BVAR with the steady-state prior has demonstrated the lowest forecast error over a two-year period, but the prediction with the Minnesota prior looks relatively stable in all horizons.