经济指标季节调整中消除节假日因素的方法

文章首先对现有季节调整方法中消除节假日因素的思路进行了简要的回顾;接着,从休闲日和工作日的独特视角,指出现有季节调整方法中消除节假日因素思路的不足,分析并归类了不同节假日因素对经济数据的不同影响;最后,根据我国一些特殊节假日的实际情况,在借鉴国外处理节假日方法的基础上,提出了消除中国节假日因素的思路。     

春节模型的设计与应用

 季节调整是经济数据预处理中非常重要的一个步骤。现有的主流季节调整方法X-12-ARIMA 和TRAMO/SEATS中都包含节假日因素的调整。由于不同的国家节假日一般不同,因此各国在进行经济数据的季节调整时,都需要结合本国的假日对季节调整方法进行修正。春节是中国最为重要而且持续时间最长的节日,具体日期可以出现在一月也可以在二月。本文基于X-12-ARIMA方法,同时考虑春节对经济指标的正负性影响效应、春节影响的变化速率以及春节效应的时长三个因素,设计了十二个不同类型的春节模型。本文应用Eviews软件和Demetra软件,采集不同的经济指标,对所设计的春节模型进行了应用研究,并根据异常值改善标准,对最佳的春节模型进行了选择与比较分析。     

统计环比指数季节调整方法与应用

由于反映前后两个时期变化的统计环比指数能及时准确体现经济运行的近期变化，再加上科学地季节调整，能及时察觉经济的趋势性变化，也能尽早采取相应的政策措施，因此，准确运用季节调整后的统计环比指数已成为加强和改善宏观调控的重要手段。针对我国主要节假日以农历计算，各公历月度之间节假日和周末分布很不固定和不均匀的问题，从理论与方法上探索了适合中国特点的X-12-AKIMA季节调整方法，并有效地在实践中加以应用。     

季节调整方法在CPI指数中的应用

文章首先对季节调整方法的发展及应用进行了说明,着重介绍了国际上使用最广泛的两种方法:X-12-ARIMA和TRAMO/SEATS;然后用X-12-ARIMA方法对我国居民消费价格指数序列进行了季节调整,探测了交易日、闰年、异常值和春节对CPI指数的影响,比较了三种季节调整模型之间的优劣并进行调整,得出了我国CPI指数只受春节因素的影响的结论,相应的最优模型也是春节效应模型;最后用这种模型对我国CPI指数进行季节调整,分离出趋势成分、季节成分和不规则成分,得到了最终的季节调整序列。     

本期导读

研究由于季节、节假日或其他特殊原因导致的非正常波动统计数据季节调整方法,解决这些统计数据如何剔除非正常因素的问题,是目前迫切需要解决的一个重要课题。《基于社会零售总额的非正常波动统计数据的季节调整》一文,采用     

国际上季节调整最新发展及对我国的思考

国际上,季节调整方法的理论和应用研究越来越受到各国政府统计官员、统计学者和其他经济研究人员的重视。最初的季节调整问题是由美国经济学家在十九世纪20年代提出,自那以后,季节调整方法的研究一直在进行,并不断取得新的进展。近年来,特别是进入上个世纪末,为了及时监控经济、金融等重要指标的基本走向和掌握经济周期转折点,预测基本发展趋势,各国政府统计机构、银行金融机构等纷纷加强对季节调整方法的研究,从而使这领域的研究又有许多新的拓展。然而,在我国,季节调整方法的研究和实践非常缺乏,至今为止,我国未公布任何经季节调整的经济…     

季节调整方法在中国的发展与应用

随着我国社会主义市场化经济程度的不断提高,人们逐渐开始使用环比数据监控宏观经济重要指标,而环比数据质量的好坏取决于对季节调整方法的了解与掌握程度.在简要介绍了季节调整方法及相应软件的发展现状之后,本文对季节调整研究在我国的发展与应用状况进行了总结.在季节调整发展部分,文章主要介绍了中国人民银行PBC版X-12-ARIMA季节调整软件和国家统计局版NBS-SA季节调整软件的特点;在季节调整应用部分,则分别以春节效应和结构时间序列模型为主线对我国季节调整文献进行了梳理.最后在小结部分,本文给出了一些建设性意见.     

基于季节SVR的节假日旅游客流量预测

准确的节假日客流量预测对旅游景区至关重要,然而受各种因素影响,节假日客流量呈现复杂非线性特点和典型季节性趋势.为了解决这种非线性和季节性问题,文章建立基于季节调整的支持向量回归模型(SSVR),并用某风景区2008～2011年节假日的日客流量验证模型的有效性.研究结果表明,SSVR预测节假日客流量效果良好,预测精度优于SVR和BPNN方法.     

农产品价格指数的季节调整方法研究

文章通过对农产品批发价格指数的季节调整,分析了农产品批发价格指数季节波动规律和经济含义。研究了春节效应在预调整中的处理方法,通过对春节效应模型的连续模拟,发现时间间隔与模型解释能力存在非线性关系,提出了适用于农产品批发价格指数春季效应预调整的最优时间间隔,以便更好的分析其季节波动特征。     

我国CPI季节调整模型预测

文章基于考虑春节效应的X-12-ARIMA季节调整模型,对我国2002年1月至2013年12月的CPI序列月度数据进行季节调整,并进行季节波动性分析及短期预测.实证结果表明:我国的CPI变动存在明显的季节性特征,春节效应对其有显著影响;CPI序列的短期波动主要是受季节性成分影响,而长期波动主要受趋势-循环成分影响;利用该模型进行短期预测效果较好,预测误差绝对值控制在1.5％之内.     

基于季节调整技术的我国物价波动实证研究

 本文首先对季节调整方法的发展及应用进行说明,并对X-12-ARIMA和TRAMO/SEATS进行方法与实证比较,得出这两种方法调整效果基本相同;其次使用X-12-ARIMA方法对我国CPI时间序列数据做了实证研究,分离出最终趋势成分、季节成分等;然后通过PBC版X-12-ARIMA分理处时间序列中的春节因素;最后通过调整后的CPI序列进行短期预测,并对其展开了一定的分析讨论。     

Issues Involved With the Seasonal Adjustment of Economic Time Series

In the first part of this article, we briefly review the history of seasonal adjustment and statistical time series analysis in order to understand why seasonal adjustment methods have evolved into their present form. This review provides insight into some of the problems that must be addressed by seasonal adjustment procedures and points out that advances in modern time series analysis raise the question of whether seasonal adjustment should be performed at all. This in turn leads to a discussion in the second part of issues invloved in seasonal adjustment. We state our opinions about the issues raised and renew some of the work of our authors. First, we comment on reasons that have been given for doing seasonal adjustment and suggest a new possible justification. We then emphasize the need to define precisely the seasonal and nonseasonal components and offer our definitions. Finally, we discuss our criteria for evaluating seasonal adjustments. We contend that proposed criteria based on empirical comparisons of estimated components are of little value and suggest that seasonal adjustment methods should be evaluated based on whether they are consistent with the information in the observed data. This idea is illustrated with an example.     

Issues Involved With the Seasonal Adjustment of Economic Time Series

In the first part of this article, we briefly review the history of seasonal adjustment and statistical time series analysis in order to understand why seasonal adjustment methods have evolved into their present form. This review provides insight into some of the problems that must be addressed by seasonal adjustment procedures and points out that advances in modem time series analysis raise the question of whether seasonal adjustment should be performed at all. This in turn leads to a discussion in the second part of issues involved in seasonal adjustment. We state our opinions about the issues raised and review some of the work of other authors. First, we comment on reasons that have been given for doing seasonal adjustment and suggest a new possible justification. We then emphasize the need to define precisely the seasonal and nonseasonal components and offer our definitions. Finally, we discuss criteria for evaluating seasonal adjustments. We contend that proposed criteria based on empirical comparisons of estimated components are of little value and suggest that seasonal adjustment methods should be evaluated based on whether they are consistent with the information in the observed data. This idea is illustrated with an example.     

中国季度GDP的季节调整：结构时间序列方法

 内容提要：本文利用结构时间序列方法讨论了中国季度GDP的季节调整问题,从季节单位根、季节自相关、周期自相关等多个方面对不同季节模式的调整结果进行了比较。结论认为,随机虚拟变量形式和三角函数形式得到的调整结果非常相似;结构时间序列方法更好地捕捉到了时变季节特征,明显优于X-11和SEATS方法;非高斯稳健季节调整的结果表明,高斯结构时间序列方法具有较好的稳定性。     

基于季节调整的CPI统计环比指数研究

本文首先阐述了季节调整与统计环比指数的必要性,简要介绍了X-12-ARIMA与TRAMO/SEATS季节调整原理,然后运用X-12-ARIMA程序对中国1997年1月至2010年5月CPI月度数据进行季节调整,再运用TRAMO/SEATS方法解决季节调整程序中中国春节因素问题。接着由季节调整后的数据计算得到月环比CPI,对月环比CPI和同比增加率进行了比较,结果显示月环比CPI领先同比CPI。最后利用TRAMO/SEATS程序建立ARIMA模型（210）（011）进行了24个月的预测,预测结果显示,未来24个月内我国消费者物价指数温和上升,不会发生大的通货膨胀,但是存在一定的通胀压力。     

我国居民消费季节调整和节日效应测算

Bayes季节调整方法因有坚实的理论基础,调整效果优于其它方法等,目前正日益受到广泛的重视与应用。本文将Bayes季节调整模型引入国内,同时在模型中补充贸易日和闰年的影响。用R软件的Timsac包中的Bayesian程序实现对社会消费品零售额的季节调整和环比增长率测算,表明长期我国社会消费品零售总额具有稳定的指数增长趋势和U型季节特征,得到的月环比增长率反应灵敏。通过季节指数抛物线拟合,得到“五一”和“十一”节日经济效应和比例。总体上“五一”的节日效应显著,“十一”仍有正面效应,但影响不显著。     

Multivariate Seasonal Adjustment,Economic Identities,and Seasonal Taxonomy

This article extends the methodology for multivariate seasonal adjustment by exploring the statistical modeling of seasonality jointly across multiple time series, using latent dynamic factor models fitted using maximum likelihood estimation. Signal extraction methods for the series then allow us to calculate a model-based seasonal adjustment. We emphasize several facets of our analysis: (i) we quantify the efficiency gain in multivariate signal extraction versus univariate approaches; (ii) we address the problem of the preservation of economic identities; (iii) we describe a foray into seasonal taxonomy via the device of seasonal co-integration rank. These contributions are developed through two empirical studies of aggregate U.S. retail trade series and U.S. regional housing starts. Our analysis identifies different seasonal subcomponents that are able to capture the transition from prerecession to postrecession seasonal patterns. We also address the topic of indirect seasonal adjustment by analyzing the regional aggregate series. Supplementary materials for this article are available online.     

Maximum entropy extreme‐value seasonal adjustment

Some economic series in small economies exhibit meagre (i.e. non‐positive) values, as well as seasonal extremes. For example, agricultural variables in countries with a distinct growing season may exhibit both of these features. Multiplicative seasonal adjustment typically utilises a logarithmic transformation, but the meagre values make this impossible, while the extremes engender huge distortions that render seasonal adjustments unacceptable. To account for these features, we propose a new method of extreme‐value adjustment based on the maximum entropy principle, which results in replacement of the meagre values and extremes by optimal projections that utilise information from the available time series dynamics. This facilitates multiplicative seasonal adjustment. The method is illustrated in the New Zealand agricultural series.     

A comparison of indicators for evaluating x-11-arima seasonal adjustment

Summary The evaluation of the performance of seasonal adjustment procedures is an issue of practical importance in view of the unobservable nature of the components. Looking at just one indicator when judging the overall quality of a procedure may be misleading, even though this is common practice when many series are involved. The main purpose of this paper is to compare the information content of different synthetic indicators with reference to the X-11-ARIMA procedure. Sixty-six different types of monthly seasonal series are generated and the seasonal component then extracted by carrying out X-11-ARIMA with standard options. The correlation between the pseudo-true error for each series and various synthetic indicators allows us to compare the latter's reliability, under both the hypotheses of minimum and maximum variance of the pseudo-true seasonal component. We show that the overall quality indexQ-the indicator most commonly adopted by users of the X-11-ARIMA-is always outperformed by the simpler diagnostics based on the stability of the estimates. In particular, the “sliding-spans” indicator, proposed by Findley et al. (1990) and included in the diagnostics of the new X-12 procedure, shows a much stronger correlation with the pseudo-true error in the seasonal adjustment. We also show that the total forecasting errors in the one-year-ahead extrapolation of the seasonal component have a good informative power and perform almost as well as the “sliding-spans” indicator.