Features of forecasting macroeconomic indicators based on the use of the mean-adjusted BVAR model
Abstract
The article investigates specific features of forecasting macroeconomic indicators using the mean-adjusted BVAR model. The BVAR model is widely used for analyzing economic time series, but its predictive ability can be improved by including an adjustment for the average value. The authors analyze the effectiveness of forecasting based on the mean-adjusted BVAR model using the example of various macroeconomic indicators. The study showed that the mean-adjusted BVAR model is more effective than other models for forecasting inflation, industrial production index and money supply. It copes particularly well with long-term forecasts and surpasses the traditional BVAR model due to the updated specification. The scientific novelty of the study lies in the systematic selection of the optimal hyperparameter for the a priori distribution of Minnesota and the comparison of the predictive power of mean-adjusted BVAR with competing models based on Russian data. The results of the work will help to improve the quality of economic forecasts and improve the efficiency of decision-making in an unstable economic environment.
About the Authors
I. A. Eremina
Peter the Great St. Petersburg Polytechnic University
Russian Federation
Russian Federation
Irina A. Eremina – Doctor of Economics, Associate Professor, Professor of the Higher School of Engineering and Economics
St. Petersburg
V. V. Vallask
Peter the Great St. Petersburg Polytechnic University
Russian Federation
Russian Federation
Vladimir V. Vallask – postgraduate student of the Higher School of Engineering and Economics
St. Petersburg
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Review
For citations:
Eremina I.A., Vallask V.V. Features of forecasting macroeconomic indicators based on the use of the mean-adjusted BVAR model. Vestnik of Samara State University of Economics. 2024;(11):22-34. (In Russ.)
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