Including the Information Background into the DSGE Model of the Russian Economy with Adaptive Learning
https://doi.org/10.25205/2542-0429-2023-23-4-60-82
Abstract
Within the framework of this study, a method is being developed for applying the results of the information background analysis model to the expectations formation model of adaptively learning economic agents in the general formulation of the DSGE model. This method is tested on a small DSGE model of the Russian economy with adaptive learning, developed by us at the IEIE SB RAS. Using this model as an example, we show that the proposed method improves the fit of the data simulated by the model to economic statistics, which makes it possible to use this model to predict macroeconomic indicators, comparing different scenarios for economic development depending on the future information background sentiment. We conclude that news flow management has an impact on the economy performance and can potentially be used as an element of economic policy, whose consequences can be evaluated using our method. The versatility of the method proposed in this paper allows its application to be extended to a wide range of DSGE models used by central banks in most countries of the world.
Keywords
DSGE-modeling,
adaptive learning,
sentiment analysis,
neural networks,
machine learning,
Russian economy,
forecasts
Supporting agencies: The publication is prepared within the project “Methods and models of Russian economy development strategy justification under changing macroeconomic reality”, № 0260-2021-0008 according to the research plan of the IEIE SB RAS.
About the Authors
D. V. Kolyuzhnov
Novosibirsk State University; Institute of Economics and Industrial Engineering SB RAS
Russian Federation
Russian Federation
Dmitriy V. Kolyuzhnov, PhD (Economics), Associate Professor; Researcher,
Novosibirsk.
Scopus ID 55940049500.
E. D. Kolyuzhnov
Novosibirsk State University
Russian Federation
Russian Federation
Egor D. Kolyuzhnov, 4th year Undergraduate Student, Information Technologies Department,
Novosibirsk.
M. V. Lyakhnova
Institute of Economics and Industrial Engineering SB RAS; Bank of Russia
Russian Federation
Russian Federation
Margarita V. Lyakhnova, Postgraduate Student; Chief economist,
Novosibirsk; Moscow.
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Review
For citations:
Kolyuzhnov D.V., Kolyuzhnov E.D., Lyakhnova M.V. Including the Information Background into the DSGE Model of the Russian Economy with Adaptive Learning. World of Economics and Management. 2023;23(4):60-82. (In Russ.) https://doi.org/10.25205/2542-0429-2023-23-4-60-82
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