Sharipova Nilufar Hikmatullayevna
PhD, Tashkent State University of Economics
associate professor of the Department “Banking Account and Audit”
Tashkent, Uzbekistan. email: [email protected]
ORCID: 0000-0002-4892-9922
Abstract: The article analyzes the changes in the macroeconomic indicators of the country, their relationship with the use of econometric methods based on the influence of indicators of financial stability of banks. With the help of the VAR model, forecast indicators are determined, and appropriate conclusions are drawn. When implementing a short-term forecast of a country’s economic growth, many central banks use the relationship equation. The main essence of such relationship equations is to reflect the target indicator in a model in which several changing factors are associated with the indicators. Many studies confirm that such models provide more accurate predictive data than a simple model. In their research, Baffigi, Golinelli, and Parigi tried to determine the most appropriate model for forecasting GDP growth in the European Union region from 1980 to 2002 and concluded that the interaction equations as a result of the study provide more accurate predictive indicators than other models. In another group of studies, the correlation equation is used as an indicator of supply and demand to predict GDP. For example, N.Pinkwart in his study estimates the components of GDP in terms of production and consumption using the equations of interoperability. According to the results of the study, this approach will help to more accurately express short-term forecast indicators, forecasting GDP from the point of view of supply and demand will increase the level of accuracy. In his research, B.Bernanke, J.Boivin, P.Eliasz proposed using the factor-vector autoregression model (FAVAR) to analyze the effectiveness of monetary policy. The method proposed in this research paper will help to qualitatively analyze the impact of monetary policy on macroeconomic indicators. The authors concluded that adding factors to the model increases the accuracy of forecasting macroeconomic indicators. The study by K.Astveit, K.R. Gerdrup, A.Jore and L.Thorsrud argues that the Central Bank of Norway uses models such as VAR, AR, and vector error correction model (VECM) for short-term inflation forecasting. In this case, the main indicator for the short-term inflation forecast is the high level of data compatibility with real data. In his research, A. Andreev analyzed the effectiveness of using a joint method to implement a short-term inflation forecast in the Russian banking system. Various forecasting models have been applied, such as VAR, BVAR2, RW, LSTAR, UC, each of which provides a generalized inflation forecast.
Keywords: The bank’s core capital, net profit, interest-free income, gross domestic product, VAR model, forecast indicators.