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传统季节调整方法对时间序列数据进行季节调整时,往往假定误差项为白噪声,不考虑其序列相关关系。为了进行更准确地季节调整分析,本文从连续性抽样调查的角度出发,研究基于平衡轮换样本调查的抽样误差对季节调整的影响,建立一般化的季节调整模型,利用卡尔曼滤波进行参数估计,并从预测误差、误差方差等角度评价模型精度。最后以中国城镇住户调查采用的12~0平衡轮换模式为例,对考虑抽样误差结构特征的季节调整模型进行实证分析,验证这套季节调整方法的有效性。 相似文献
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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. 相似文献
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季节调整使子年度数据可比,有利于环比增长率测算和经济监测。国际上季节调整模型众多,模型选择是季节调整的首要任务。以国际常用的X-12-ARIMA和TRAMO/SEATS模型的选择为目标、以两模型理论差异分析为基础、以中国2001—2010年的月度CPI数据为样本,通过谱分析方法检验剩余季节性、幂等、平滑间距和修正历史等方法检验模型稳定性、通过Friedman和Kruskal-Wallis等非参数方法检验季节稳定性,得出模型之间更具体的差异,为满足实践需要进行模型选择提供科学依据。 相似文献
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本文的主要工作是从频域的角度对季节调整中“季节滤子”的设计及估计问题进行研究。通过将直接信号提取(DSEF)方法引入到季节调整的应用之中,突破现有季节调整方法中仅能处理季度或月度数据的限制,且该方法下季节调整后的序列是理论季节调整后序列的“均方误差”最小估计。将DSEF方法应用于对中国季度进出口总额序列的季节调整分析中。分析结果显示,相比于X-11和SEATS方法,DSEF方法季节调整结果的离差较小且稳健性较好。 相似文献
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针对季节调整方法如X-11等调整结果不利于解释,及其方法本身没有考虑我国像春节等季节性特点的不足,文章建立起一般的季节时间序列模型,另外,针对季节周期的主观诊断,文章建立起辅助回归模型,较为客观的诊断时间序列的季节周期。结合我国铁路客运量的实证分析,预测结果表明:未来10月铁路客流量较大,相反,11月和12月客运量较小,这点和历史数据的特征十分类似,说明建立的模型较合适。 相似文献
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季节调整方法综述及比较 总被引:12,自引:0,他引:12
一、前言所谓季节调整,就是将某一统计指标的时间序列中的季节性因素和偶然性因素剔除,从而使经过季节调整的时间序列能够较为准确地反映出社会经济运行基本态势。早在20世纪的上半叶人们就开始了从时间序列中分解季节因素、调整季节变动的尝试。季节调整的问题首先是由美国经济学家1919年提出的,此后,有关季节调整的方法不断的出现和改进。1931年麦考利(Macauley)提出了用移动平均比率法进行季节调整,成为季节调整方法的基础。1954年Shiskin在美国普查局首先开发了在计算机上运行的程序对时间序列进行季节调整,称为X1,此后,季节调整的方… 相似文献
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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. 相似文献
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This work presents a framework of dynamic structural models with covariates for short-term forecasting of time series with complex seasonal patterns. The framework is based on the multiple sources of randomness formulation. A noise model is formulated to allow the incorporation of randomness into the seasonal component and to propagate this same randomness in the coefficients of the variant trigonometric terms over time. A unique, recursive and systematic computational procedure based on the maximum likelihood estimation under the hypothesis of Gaussian errors is introduced. The referred procedure combines the Kalman filter with recursive adjustment of the covariance matrices and the selection method of harmonics number in the trigonometric terms. A key feature of this method is that it allows estimating not only the states of the system but also allows obtaining the standard errors of the estimated parameters and the prediction intervals. In addition, this work also presents a non-parametric bootstrap approach to improve the forecasting method based on Kalman filter recursions. The proposed framework is empirically explored with two real time series. 相似文献
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Ngai Hang Chan 《Revue canadienne de statistique》1989,17(3):279-284
A time series is said to be nearly nonstationary if some of its characteristic roots are close to the unit circle. For a seasonal time series, such a notion of near-nonstationarity is studied in a double-array setting. This approach not only furnishes a natural transition between stationarity and nonstationarity, but also unifies the corresponding asymptotic theories in a seasonal-time-series context. The general theory is expressed in terms of functionals of independent diffusion processes. The asymptotic results have applications to estimation and testing in a nearly nonstationary situation and serve as a useful alternative to the common practice of seasonal adjustment. 相似文献
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Lon-Mu Liu 《统计学通讯:理论与方法》2013,42(6):2279-2288
This paper proposes an identification method of ARIMA models for seasonal time series using an intermediary model and a filtering method. This method is found to be useful when conventional methods, such as using sample ACF and PACF, fail to reveal a clear-cut model. This filtering identification method is also found to be particularly effective when a seasonal time series is subjected to calendar variations, moving-holiday effects, and interventions. 相似文献
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Daniel Thorburn 《Journal of applied statistics》2014,41(9):2075-2091
The problem of whether seasonal decomposition should be used prior to or after aggregation of time series is quite old. We tackle the problem by using a state-space representation and the variance/covariance structure of a simplified one-component model. The variances of the estimated components in a two-series system are compared for direct and indirect approaches and also to a multivariate method. The covariance structure between the two time series is important for the relative efficiency. Indirect estimation is always best when the series are independent, but when the series or the measurement errors are negatively correlated, direct estimation may be much better in the above sense. Some covariance structures indicate that direct estimation should be used while others indicate that an indirect approach is more efficient. Signal-to-noise ratios and relative variances are used for inference. 相似文献
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《Journal of Statistical Computation and Simulation》2012,82(6):643-651
This article builds on the test proposed by Lyhagen [The seasonal KPSS statistic, Econom. Bull. 3 (2006), pp. 1–9] for seasonal time series and having the null hypothesis of level stationarity against the alternative of unit root behaviour at some or all of the zero and seasonal frequencies. This new test is qualified as seasonal-frequency Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test and it is not originally supported by a regression framework. The purpose of this paper is twofold. Firstly, we propose a model-based regression method and provide a clear illustration of Lyhagen's test and we establish its asymptotic theory in the time domain. Secondly, we use the Monte Carlo method to study the finite-sample performance of the seasonal KPSS test in the presence of additive outliers. Our simulation analysis shows that this test is robust to the magnitude and the number of outliers and the statistical results obtained cast an overall good performance of the test finite-sample properties. 相似文献
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《Journal of Statistical Computation and Simulation》2012,82(9):843-851
This paper considers spurious regression between two different types of seasonal time series: one with a deterministic seasonal component and the other with a stochastic seasonal component. When one type of seasonal time series is regressed on the other type and they are independent of each other, the phenomenon of spurious regression occurs. Asymptotic properties of the regression coefficient estimator and the associated regression ‘t-ratio’ are studied. A Monte Carlo simulation study is conducted to confirm the phenomenon of spurious regression and spurious rejection of seasonal cointegration for finite samples. 相似文献
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Seasonal fractional ARIMA (ARFISMA) model with infinite variance innovations is used in the analysis of seasonal long-memory time series with large fluctuations (heavy-tailed distributions). Two methods, which are the empirical characteristic function (ECF) procedure developed by Knight and Yu [The empirical characteristic function in time series estimation. Econometric Theory. 2002;18:691–721] and the Two-Step method (TSM) are proposed to estimate the parameters of stable ARFISMA model. The ECF method estimates simultaneously all the parameters, while the TSM considers in the first step the Markov Chains Monte Carlo–Whittle approach introduced by Ndongo et al. [Estimation of long-memory parameters for seasonal fractional ARIMA with stable innovations. Stat Methodol. 2010;7:141–151], combined with the maximum likelihood estimation method developed by Alvarez and Olivares [Méthodes d'estimation pour des lois stables avec des applications en finance. Journal de la Société Française de Statistique. 2005;1(4):23–54] in the second step. Monte Carlo simulations are also used to evaluate the finite sample performance of these estimation techniques. 相似文献
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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. 相似文献
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《商业与经济统计学杂志》2013,31(1):98-127
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. 相似文献