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Issue title: Fuzzy model for human autonomous computing in extreme surveillance and it’s applications
Guest editors: Varatharajan Ramachandran
Article type: Research Article
Affiliations: School of Politics and Public Administration, University of Political Science and Law, Beijing, China
Correspondence: [*] Corresponding author. Nan Lin, School of Politics and Public Administration, University of Political Science and Law, Beijing, China. E-mail: Linnan_CUPL@163.com.
Abstract: Our country’s economic growth is overly dependent on government investment, and bank credit and money supply lack a strict monitoring mechanism. Therefore, rapid economic growth is always accompanied by inflation risks. In order to study the effect of inflation impact analysis, based on machine learning algorithms, this paper combines artificial intelligence technology to analyze the impact of inflation expectations, and constructs the central bank information disclosure index and inflation expectations index. Moreover, this paper will perform ADF unit root test on the data. In addition, after confirming that the data is stable, this paper uses the Markov Regime Transfer Vector Autoregressive (MSVAR) model and state-dependent impulse response function to test and analyze the effect of China’s central bank communication in guiding the formation of inflation expectations. Through research, we can see that the machine learning algorithm constructed in this paper has significant effects, which can provide a reference for the analysis of the impact of inflation expectations.
Keywords: Machine learning, intelligent model, inflation, improved algorithm
DOI: 10.3233/JIFS-189495
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6581-6592, 2021
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