XUE Qinglin,WEI Zikun,HAN Qiao,et al.Study on Price Fluctuation Characteristics,Prediction and Early Warning of Qi Chrysanthemum in Post Epidemic Era[J].Northern Horticulture,2022,(11):135-14.[doi:10.11937/bfyy.20214445]
后疫情时代祁菊价格波动特征及预测预警
- Title:
- Study on Price Fluctuation Characteristics,Prediction and Early Warning of Qi Chrysanthemum in Post Epidemic Era
- Keywords:
- price of Qi chrysanthemum; COVID-19; decomposition of price fluctuation; prediction and early warning
- 文献标志码:
- A
- 摘要:
- 祁菊作为治疗新冠肺炎“三药三方”的主要成分,近年来波动频繁,为探究祁菊价格波动规律促进祁菊产业有序健康发展,该研究选取2014年7月至2020年11月祁菊月度价格数据,通过X-12季节调整法和H-P滤波法对祁菊价格序列进行分解,求出不同波动成分的贡献率,再借助ARIMA模型与黑色预警模型,对祁菊价格进行预测预警。结果表明:影响价格波动的因素中趋势性因素处于主导地位,随机因素中周期性因素的贡献率最大,为11.84%,季节性因素与不规则性因素均为3.96%,且三者均呈现下降趋势。随后预测了后续15个月的价格,出现1次负向重警,3次负向轻警,其余的指数波动率也均为负值,表明未来的15个月祁菊价格下行压力较大,最后提出提升产业融合度、价格监测度与风险分担度的对策建议。
- Abstract:
- Qi chrysanthemum,as novel coronavirus pneumonia ‘three drugs three parties’ main ingredient,fluctuated frequently in recent years.In order to explore the law of Qi chrysanthemum price fluctuation and promote the orderly and healthy development of Qi chrysanthemum industry,we selected monthly data of Qi chrysanthemum from July 2014 to November 2020,decomposed the price sequence of Qi chrysanthemum by X-12 seasonal adjustment method and H-P filtering method,and found out the contribution rate of different fluctuation components.Then with the help of ARIMA model and black early warning model,the price of Qi chrysanthemum was predicted and warned.The results showed that among the factors affecting price fluctuation,the trend factor was in the dominant position.Among the random factors,the contribution rate of periodic factor was the largest,which was 11.84%,and the contribution rate of seasonal factor and found factor was 3.96%.Subsequently,the price in the next 15 months was predicted.There was one negative heavy alarm and three negative light alarms,and the other index volatility were also negative,indicating that there was great downward pressure on Qi chrysanthemum price in the next 15 months.Finally,the countermeasures and suggestions to improve the degree of industrial integration,price monitoring and risk sharing are put forward.
参考文献/References:
[1]常征宇,王树进.我国中药材价格波动影响因素的实证研究:基于因子分析和向量自回归模型[J].科技与经济,2015,28(5):101-105.[2]马宏阳,赵霞.中国小宗农产品价格波动特征的实证分析:以大蒜为例[J].农业技术经济,2021(6):33-48.[3]李苏,宝哲.我国猪肉价格波动特征及预测研究[J].价格理论与实践,2020(6):80-83,153.[4]卞靖,陈曦.中国蔬菜价格波动的特征、原因及调控思路研究[J].宏观经济研究,2020(4):142-152.[5]茅鸯对,常峰.基于GM(1,1)预测模型的中药材价格指数预测[J].中国药房,2014(23):2200-2202.[6]常峰,茅鸯对.基于ARIMA预测模型的中药材价格预警研究[J].中国中药杂志,2014(9):1721-1723.[7]李化.中药材价格传导研究[J].卫生经济研究,2015(10):58-61.[8]任长秋.中药材价格变动成因及影响[J].人民论坛,2011(1):142-143.[9]魏子鲲,韩乔,薛庆林,等.河北省中药材质量追溯体系浅析[J].合作经济与科技,2021(2):110-112.[10]张晋之,杨元娟,许燕.中药材价格波动的原因及优化策略[J].价格月刊,2016(2):35-38.[11]胡友,祁春节.基于HP滤波模型的农产品价格波动分析:以水果为例[J].华中农业大学学报(社会科学版),2014(4):57-61.[12]魏子鲲,韩乔,王亚荣,等.河北省中药材产业发展的SWOT分析[J].河北农业科学,2021,25(3):34-36,49.[13]付雅美,李泽华,卢秀茹.基于Logit模型的河北省棉花市场价格波动影响因素分析[J].河北农业大学学报(社会科学版),2019,21(5):43-48.[14]石自忠,王明利,高海秀.中国猪肉价格波动的双重非对称效应:基于MS-GARCH类模型[J].农林经济管理学报,2019,18(5):675-683.
相似文献/References:
[1]宋昌昊.新冠肺炎疫情对中国花卉产业发展的影响[J].北方园艺,2020,44(20):142.[doi:10.11937/bfyy.20200921]
SONG Changhao.Effects of COVID-19 on the Flower Industry Development in China[J].Northern Horticulture,2020,44(11):142.[doi:10.11937/bfyy.20200921]
[2]王文青,王建忠,王斌.全球新冠肺炎背景下加快我国连翘产业发展的策略[J].北方园艺,2021,(10):136.[doi:10.11937/bfyy.20203381]
WANG Wenqing,WANG Jianzhong,WANG Bin.Strategies to Accelerate the Development of China′s Forsythia Industry in the Context of Global COVID-19[J].Northern Horticulture,2021,(11):136.[doi:10.11937/bfyy.20203381]
备注/Memo
第一作者简介:薛庆林(1960-),男,博士,教授,博士生导师,现主要从事中药材产业经济等研究工作。E-mail:weizikun6481@163.com.责任作者:王建忠(1965-),男,博士,教授,博士生导师,现主要从事农业产业经济等研究工作。E-mail:wei754912388@163.com.基金项目:河北省现代农业产业技术体系中药材产业创新建设资助项目(HBCT2018060301);河北省高等学校人文社会科学研究资助项目(SD181041);河北省教育厅省级研究生创新资助项目(CXZZBS2019104)。收稿日期:2021-11-04