Application of Bayesian Vector Autoregressive Model in Regional Economic Forecast

The Bayesian vector autoregressive (BVAR) model introduces the statistical properties of variables as the prior distribution of the parameters into the traditional vector autoregressive (VAR) model, which can overcome the problem of too little freedom. The BVAR model established in this paper can ov...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2021, Vol.2021 (1)
Main Authors: Ma, Jinghao, Shang, Yujie, Zhang, Hongyan
Format: Article
Language:English
Subjects:
Accuracy
Autoregressive models
Bayesian analysis
Econometrics
Economic development
Economic forecasting
Economic forecasts
Economic models
Macroeconomics
Methods
Model accuracy
Parameter estimation
Regional development
Regression analysis
Time series
Variables
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Summary:The Bayesian vector autoregressive (BVAR) model introduces the statistical properties of variables as the prior distribution of the parameters into the traditional vector autoregressive (VAR) model, which can overcome the problem of too little freedom. The BVAR model established in this paper can overcome the problem of short time series data by using prior statistical information. In theory, it should have a good effect in China’s regional economic forecasting. Most regional forecasting model literature lacks out-of-sample forecasting error evaluation research in the real sense, but our early forecasts of major economic indicators provide an excellent opportunity for this paper to evaluate the actual forecast errors of the BVAR model in detail. The analysis in this paper shows that the prediction error of the BVAR model is very small and the prediction ability is very satisfactory. At the same time, this article also analyzes and points out the direction of efforts to further improve the prediction accuracy of the BVAR model.
ISSN:1076-2787
1099-0526
DOI:10.1155/2021/9985072