Taking into account non-price competition in the course of forecasting Russian corporate crediting market

Analysis of the financial statistics of recent years suggests that the credit market is one the most stable segments of the Russian financial and credit system. Therefore, the prediction of the most important macroeconomic indicators is impossible without forecasting the bank crediting indicators. The purpose of this article is to identify the significance of the statistical indicators that are new for Russia -indexes of bank lending (hereinafter - BCC) for forecasting the credit market conditions. The article describes the econometric model of vector autoregression, which allows forecasting indicators of the corporate crediting market and includes indexes of BCC. The model is compiled by the author and based on foreign experience of incorporating indexes of BCC in econometric models. Theoretical basis for the model is the production and organizational approach to banking activity modeling. This approach assumes that the sole purpose of functioning of a bank in the credit and deposit markets is to maximize profits. The model includes five equations, the unknown parameters of which were assessed using a Two-Stage OLS. All equations are checked for the presence of multicollinearity of the regressors and heteroscedasticity and autocorrelation of the residuals. The forecast is short-term with the forecasting horizon of two quarters. It is constructed for the two main indicators of the credit market: the interest rate on credits and the rate of growth of entities' debt to the banking sector in Russia. The quality of forecast is assessed using the mean absolute percentage error of the forecast. For both indicators it does not exceed 25% of their mean value of the actual statistics. This accuracy of the forecast was achieved largely due to the inclusion of the indices of BCC in the model.