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The Application of Additive Seasonal Model in the Analysis of Domestic Credit Scale

Jie Chen

Abstract


To establish statistical model of measurement, this paper analyses the five major domestic economic data whether impact on domestic credit amount, for the purpose of the data cointegration test to eliminate the spurious regression result in error, and through the OLS regression estimate and error correction model is established for dynamic balance of feedback in the short term, then use VAR impulse response function to feedback the long-term dynamic equilibrium, the last three parameters exponential smoothing model is set up to predict the future scale of the credit. According to the conclusion, the total domestic credit scale is a series with cycle fluctuation, seasonal effect and long-term trend. Domestic money supply and domestic fiscal revenue are the main factors affecting the scale of domestic credit. In other words, the fluctuation of domestic credit model tends to return to the normal level, which is balanced and restricted by the medium and long term stable co-integration relationship of the model. To absorb the impact of domestic fiscal revenue or an increase in the domestic money supply, banks adjust for changes in the size of credit, and then show little change. In the short term, the two variables significantly affect the credit scale, but the long-term impact is not significant. Under the condition of constant economic environment, it will still show a steady rise in the future, and the trend of annual cycle is increasing month by month.

Keywords


Time series model; Domestic credit scale; Holt-Winters smoothing model; Addition of seasonal models

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References


Zhu Z, Liu D. The Adaptation between Insurance Industry and Economic Development: Empirical Research Based on Principal Component Regression Method [J]. Statistics Education 2009; 000(006): 21-25.

Xu X. Analysis of Commercial Bank Credit Scale and Risk Management Based on Time Series Model [J]. Modern

Business 2019; (24).

Zhao B, Wang X. Research on fi nancial risk prediction of listed companies based on intuitionistic time fuzzy series [J]. Journal of Liaoning Technical University (Social Science Edition) 2020; 119(01): 20-26.

Liu C, Yao J. The application of time series analysis in consumer price index prediction [J]. Modern Commerce and Trade Industry 2010; 000(016): 9-10.

Zhang L. Prediction Analysis and Research on the Trend of Industrial Value Added in China by Time Series Analysis [J]. Statistics and Consulting 2010; 000(002): 16-18.

Shi Y, Liu X. The application of the product season model in the time series analysis of temperature and precipitation [C] The annual meeting of the Chinese Society for High-tech Cross Research in Mathematical Mechanics and Physics 2010.

Wang Z, Zhang H. Analysis and Prediction of Industrial Time Series in China [J]. Theory New Exploration 2004; 8.

Guo R. Study on the Relationship between Money Supply (M1) and GDP Based on ADF, Cointegration and Distributed Lag Model [J]. China Business Journal 2009; 02.

Cai G. Analysis of the Infl uencing Factors of Money Supply [J]. Economic Forum 2012; 506.

Wang Z, Zhang H. Analysis and Forecast of Industrial Time Series in China [J], New Theory 2004; 8.




DOI: http://dx.doi.org/10.18686/fm.v6i2.3290

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