春节模型的效应期识别方法研究

陈飞, 怀雅男

系统工程理论与实践 ›› 2019, Vol. 39 ›› Issue (4) : 1031-1041.

PDF(1103 KB)
PDF(1103 KB)
系统工程理论与实践 ›› 2019, Vol. 39 ›› Issue (4) : 1031-1041. DOI: 10.12011/1000-6788-2018-1988-11
论文

春节模型的效应期识别方法研究

    陈飞, 怀雅男
作者信息 +

Research on the influence period length identification of the Spring Festival model

    CHEN Fei, HUAI Yanan
Author information +
文章历史 +

摘要

春节模型的参数设定目前更多依赖于研究者的主观判断.为解决这一问题,本文首先介绍了春节因素调整的一般过程以及模型设定和检验的相关细节.在此基础上,提出一种基于"循环遍历"方式和序贯检验方法来自动选择春节模型的最优参数组合的新思路.并以社会消费品零售总额月度序列的春节模型的最优效应期长度识别为例,检验该方法的可行性和有效性.实证结果显示,基于季节峰值、离群值点、Q统计量、AICC值和BIC值等统计量的序贯检验能够有效识别出春节模型的最优效应期长度,进而改善春节模型的季节调整性能.

Abstract

Nowadays, the parameter setting of the Spring Festival model mainly depends on subjective judgement for researchers. To solve the problem, firstly, the paper introduces factor-adjusted general process on Spring Festival and relevant details for model specification and model inspection. On this basis, the paper puts forward new ideas on selecting automatically Spring Festival model with optimal parameters based on "loop through" and sequential test method. Taking Spring Festival model's optimal effective phase length recognition of total retail sales of social consumer goods as an example, we examine its feasibility and validity. The empirical results show that the sequential test method based on seasonal peaks, outliers, Q statistics, AICC values, and BIC values can effectively identify the optimal infulence period length of the Spring Festival model, which can efficiently improve seasonal adjustment performance on Spring Festival model.

关键词

春节模型 / 效应期长度 / 序贯检验方法

Key words

Spring Festival model / influence period length / sequential test method

引用本文

导出引用
陈飞 , 怀雅男. 春节模型的效应期识别方法研究. 系统工程理论与实践, 2019, 39(4): 1031-1041 https://doi.org/10.12011/1000-6788-2018-1988-11
CHEN Fei , HUAI Yanan. Research on the influence period length identification of the Spring Festival model. Systems Engineering - Theory & Practice, 2019, 39(4): 1031-1041 https://doi.org/10.12011/1000-6788-2018-1988-11
中图分类号: O212   

参考文献

[1] Bell W R, Hillmer S C. Modelling time series with calendar variation[J]. Journal of the American Statistical Association, 1983, 78(383):526-534.
[2] Monsell B. The X-13A-S seasonal adjustment program[C]//Proceedings of the 2007 Federal Committee on Statistical Methodology Research Conference, http://www.fcsm.gov/07papers/Monsell.Ⅱ-B.pdf.
[3] Lin J L, Liu T S. Modeling lunar calendar holiday effects in Taiwan[J]. 台湾经济政策与预测, 2003, 33(2):1-37.
[4] Rehomme M G, Rejeb A B. Modelling moving feasts determined by the Islamic calendar:Application to macroeconomic Tunisian time Series[J]. Metodološki zvezki, 2008, 5(2):173-211.
[5] Shuja N, Lazim M A, Wah Y B. Moving holiday effects adjustment for Malaysian economic time series[J]. Journal of the Department of Statistics, 2007, 1(1):35-50.
[6] Cleveland W S, Devlin S J. Calendar effects in monthly time series:Detection by spectrum analysis and graphical methods[J]. Journal of the American Statistical Association, 1980, 75(371):487-496.
[7] Cleveland W S, Devlin S J. Calendar effects in monthly time series:Modeling and adjustment[J]. Journal of the American Statistical Association, 1982, 77(379):520-528.
[8] 陈荣, 梁昌勇, 陆文星, 等. 基于季节SVR-PSO的旅游客流量预测模型研究[J]. 系统工程理论与实践, 2014, 34(5):1290-1296. Chen R, Liang C Y, Lu W X, et al. Forecasting tourism flow based on seasonal PSO-SVAR model[J]. Systems Engineering-Theory & Practice, 2014, 34(5):1290-1296.
[9] 李晓芳, 吴桂珍, 高铁梅. 我国经济指标季节调整中消除春节因素的方法研究[J]. 数量经济技术经济研究, 2003(4):64-67. Li X F, Wu G Z, Gao T M. Study of the methods to eliminate spring festival factor in seasonal adjustment on economic indicators of China[J]. Quantitative and Technical Economics, 2003(4):64-67.
[10] 龙勇, 苏振宇, 汪於. 基于季节调整和BP神经网络的月度负荷预测[J]. 系统工程理论与实践, 2018, 38(4):1052-1060. Long Y, Su Z Y, Wang Y. Monthly load forecasting model based on seasonal adjustment and BP neural network[J]. Systems Engineering-Theory & Practice, 2018, 38(4):1052-1060.
[11] 齐东军. 季节调整方法在货币供应量中的应用[J]. 数量经济技术经济研究, 2004(6):147-155. Qi D J. Application of seasonal adjustment method to the money supply[J]. Quantitative and Technical Economics, 2004(6):147-155.
[12] 张鸣芳, 项燕霞, 齐东军. 居民消费价格指数季节调整实证研究[J]. 财经研究, 2004(3):133-144. Zhang M F, Xiang Y X, Qi D J. An empirical study on seasonal adjustment method of residents consumer pricing index[J]. Journal of Finance and Economics, 2004(3):133-144.
[13] 贾淑梅. 货币供应量季节调整中消除春节因素的实证研究[J]. 统计研究, 2005, 22(10):63-68. Jia S M. The experimental analysis of eliminating the factor of spring festival in seasonal adjustment of monetary supply[J]. Statistical Research, 2005, 22(10):63-68.
[14] 栾惠德, 张晓峒. 季节调整中的春节模型[J]. 经济学(季刊), 2007, 6(2):707-722. Luan H D, Zhang X T. A spring festival model for seasonal adjustments[J]. China Economic Quarterly, 2007, 6(2):707-722.
[15] 陈杰.经济指标季节调整中消除节假日因素的方法[J]. 统计与决策, 2008(3):24-26. Chen J. Study of the methods to eliminate festival factor in seasonal adjustment on economic indicators[J]. Statistics and Decision, 2008(3):24-26.
[16] 贺凤羊, 刘建平. 如何对中国CPI进行季节调整——基于X-12-ARIMA方法的改进[J]. 数量经济技术经济研究, 2011(5):110-124.He F Y, Liu J P. How to do seasonal adjustment of China's CPI[J]. Quantitative and Technical Economics, 2011(5):110-124.
[17] 石刚. 春节模型的设计与应用[J]. 统计研究, 2013, 30(1):87-95. Shi G. Design and application of the Chinese Spring Festival models[J]. Statistical Research, 2013, 30(1):87-95.
[18] Findley D F, Soukup R J. Modeling and model selection for moving holidays[R]. American Statistical Association Proceeding, October, 2000.
[19] Lothian J, Morry M. A test of quality control statistics for the X-11-ARIMA seasonal adjustment program[R]. Research Paper, Seasonal Adjustment and Time Series Staff, Statistics Canada, 1978.
[20] 中国人民银行调查统计司. 时间序列X-12-ARIMA季节调整——原理与方法[M]. 北京:中国金融出版社, 2006:370-376.The People's Bank of China Survey Statistics Division. Time series X-12-ARIMA seasonal adjustment-Principles and methods[M]. Beijing:Press of China Finance, 2006:370-376.

基金

国家社会科学基金(18BJY134);国家社会科学基金重大项目(15ZDA011);国家自然科学基金(41571121);东北财经大学创新团队项目(DUFE2017T04)
PDF(1103 KB)

448

Accesses

0

Citation

Detail

段落导航
相关文章

/