论文部分内容阅读
论文基于时间序列和随机理论,提出了消费随机预测AR-GARCH模型仿真方法,估计出社会消费的预测值与置信区间。应用AR-GARCH模型模拟出全国社会消费品零售总额历月的走势并对其预测,研究表明,该模型预测精度高,反映出我国社会消费品零售总额虽呈现出逐年上升的走势,但增幅却有平稳下降的可能,并据此提出相关政策建议,为消费政策的调整和完善提供决策依据。
Based on time series and stochastic theory, this dissertation proposes AR-GARCH model simulation method based on stochastic forecast of consumption, and estimates the forecast value and confidence interval of social consumption. The AR-GARCH model is used to simulate and forecast the calendar month of retail sales of consumer goods in the whole country. The research shows that the forecasting accuracy of the model is high, which shows that although the total retail sales of social consumer goods in our country have been increasing year by year, The possibility of declining, and accordingly put forward the relevant policy recommendations for the adjustment and improvement of consumer policies to provide decision-making basis.