蔬菜价格时间序列的分解与分析——基于北京市2002-2012年数据

Time Series Decomposition and Analysis of Vegetable Prices——Based on Data of Beijing from 2002 to 2012

基于北京市2002-2012年的蔬菜价格数据,采用时间序列分解和H-P滤波技术,将蔬菜价格分解为季节性波动、随机性波动、周期性波动和长期趋势4部分,并测算各波动成分对蔬菜价格波动的贡献率。结果发现,季节性因子特征显著,是影响蔬菜价格变化的最主要因素,平均贡献率为62.3%;2002-2012年划分为6个周期,周期的时间跨度和波动幅度均有变大的趋势,周期性波动对蔬菜价格变化的贡献率为23.6%;突发和异常事件是引起蔬菜价格随机性波动的主要诱因,随机性波动对蔬菜价格变化的贡献率为14.1%;蔬菜价格长期趋势由持续上行转为波动上行,可分为升幅较缓、升速加快和波动上行3个阶段。基于此,提出如下政策建议:完善蔬菜产业链条监测,强化信息流对物流引导作用的发挥;开展蔬菜市场预警建设,降低突发事件引起的随机性波动对市场的影响;自产蔬菜供应采取"见缝插针"的策略,在突发事件时起到平抑市场价格波动的作用等。

Based on data of vegetable prices from 2002 to 2012, this paper uses time series decomposition and H-P filtering technology to decompose vegetable prices into seasonal fluctuation,random fluctuation, cyclical fluctuation and long-term trend and it also measures the contribution rate of fluctuation elements to vegetable price fluctuations. The result shows that seasonal factor is the most important factor that result in the change of vegetable prices,with the average contribution rate of 62. 3 %. The years between 2002 and 2012 can be divided into six cycles, both time span and fluctuations of cycle are big trends; the contribution rate of cyclical fluctuations to vegetable prices change is 23. 6%. And abnormal emergency is the principal cause of vegetable prices caused by random fluctuations; the contribution rate of random fluctuation is 14. 1%. Long-term trend of vegetable price, from sustained upward trend to fluctuational upward trend, can be divided into moderate increase, quicker increase and upward volatility. Therefore, this paper proposes several policy suggestions： firstly, to improve the monitering of vegetable industry chain and strengthen the guiding role of information flow on the logistics; secondly, to carry out the construction of vegetable market warning system, and minimize the impact of random fluctuations on market caused by unexpected events. Finally, ＂must attack＂ measurement for self-production vegetable should be adopted,which can stablize price fluctuations when unexpected events take place.