Abstract
The alternative variation of variance in Stochastic Volatility (SV) models provides a big data modelling solution that is more suitable for the fluctuation process in macroeconomics for de-scribing unobservable fluctuation features. The estimation method based on Monte Carlo simula-tion shows unique advantages in dealing with high-dimensional integration problems. The statis-tical research on macroeconomic big data based on Bayesian stochastic volatility model builds on the Markov Chain Monte Carlo estimation. The critical values of the statistics can be defined exactly, which is one of the drawbacks of traditional statistics. Most importantly, the model pro-vides an effective analysis tool for the expected variable generation behaviour caused by macroe-conomic big data statistics.
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Funding
Major project of statistical research project of Shandong Province in 2019: “statistical research on macro-economic big data based on Bayesian stochastic fluctuation model: from the perspective of Monte Carlo simulation” (No.: kt1907); supported by the growth plan of young teachers in Shandong Province.
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Shan, M. (2021). Statistical Research on Macroeconomic Big Data: Using a Bayesian Stochastic Volatility Model. In: Deze, Z., Huang, H., Hou, R., Rho, S., Chilamkurti, N. (eds) Big Data Technologies and Applications. BDTA WiCON 2020 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 371. Springer, Cham. https://doi.org/10.1007/978-3-030-72802-1_7
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DOI: https://doi.org/10.1007/978-3-030-72802-1_7
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