Research Article
Statistical Research on Macroeconomic Big Data: Using a Bayesian Stochastic Volatility Model
@INPROCEEDINGS{10.1007/978-3-030-72802-1_7, author={Minglei Shan}, title={Statistical Research on Macroeconomic Big Data: Using a Bayesian Stochastic Volatility Model}, proceedings={Big Data Technologies and Applications. 10th EAI International Conference, BDTA 2020, and 13th EAI International Conference on Wireless Internet, WiCON 2020, Virtual Event, December 11, 2020, Proceedings}, proceedings_a={BDTA \& WICON}, year={2021}, month={7}, keywords={Bayesian stochastic volatility models Economic big data statistics Monte Carlo simulation algorithms}, doi={10.1007/978-3-030-72802-1_7} }- Minglei Shan
Year: 2021
Statistical Research on Macroeconomic Big Data: Using a Bayesian Stochastic Volatility Model
BDTA & WICON
Springer
DOI: 10.1007/978-3-030-72802-1_7
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.
