我国出口集装箱运价指数研究

文章运用X-12-ARIMA方法对中国集装箱运价指数序列进行了季节调整，并采用TRAMO／SEATS季节调整方法处理了节假日因素的调整问题，然后对两个模型进行了比较，利用优选出来的模型将原序列分解成了各影响因素，最后对中国出口集装箱运价指数进行了精确的短期预测。     

中国入境旅游外汇收入季节调整实证分析

旅游业的行业特点决定了中国入境旅游时间序列的发展趋势中存在明显的季节变动因素及不规则因素.本文利用X-12-ARIMA季节调整方法,把中国入境旅游外汇收入序列具体分解为长期趋势因素、季节变动因素及不规则因素,并对各构成因素作了简要分析,以期能对中国入境旅游业的变动规律给出一个相对客观的认识.     

Income smoothing from a Census X-12 perspective

Corporate income smoothing has been the focus of much attention, yet relatively little is known about the key characteristics of income-smoothing firms. To address this issue, the current study uses quarterly data with Census X-12 analysis in a novel way to identify firms where the degree of random variability in earnings is less than the degree of random variability in sales (EVAR < SVAR). Prior research views such firms as effective smoothers, since most firms have scale-free variability profiles in the opposite direction (EVAR > SVAR). Large-sample US results identify these exceptions throughout a broad cross section of firms, but smaller and less profitable firms tended to have a higher incidence rate. Results also indicate that effective smoothers exhibited higher earnings persistence.     

粮食价格波动的季节性及其形成机理

粮食价格的变化牵动着亿万人的心,也事关国家粮食的安全问题。本文从实证角度分析了我国粮食价格波动的季节性规律,利用X-13等先进统计方法进行了季节分解,得出它们的季节因子及其变化特征,并对影响粮食价格季节变化的国内外因素进行了综合分析,指出导致粮价季节性变化的显著因素和形成的机理。     

Damping seasonal factors: Shrinkage estimators for the X-12-ARIMA program

We examine the effect of damping X-12-ARIMA's estimated seasonal variation on the accuracy of its seasonal adjustments of time series. Two methods for damping seasonals are proposed. In a simulation experiment, we generated time series data for each of 90 distinct experimental conditions that, in aggregate, characterize the variety of monthly series in the M3-competition. X-12-ARIMA consistently overestimated the actual seasonal variation by an amount consistent with statistical theory. Damping seasonals reduced X-12-ARIMA's estimation error by as much as 79% and under no conditions was estimation error increased beyond a trivial amount. Improvement depended primarily on the degree to which random variation in a series dominated seasonal variation. When the multiplicative X-12-ARIMA model did not match the data-generating model, overestimation was less for trend series than for series with no trend; otherwise the presence of trend had no discernible effect. One of the proposed methods was somewhat more accurate and robust, but more complex, than the other. In an analysis of real data—the 1428 monthly series of the M3-competition-damping X-12-ARIMA seasonals prior to forecasting (1) reduced the average forecasting MAPE by 4.9–1.4% and (2) improved forecasting accuracy for 59–65% of the series, depending on the forecasting horizon. This research suggests that damping X-12-ARIMA seasonals leads to more accurate seasonal adjustments of time series, thus providing a more reliable basis for policy-making, forecasting, and the evaluation of forecasting methods by researchers.     

Implementation issues on shrinkage estimators for seasonal factors within the X-11 seasonal adjustment method

This commentary on Miller and Williams [Intl. J. Forecast. 20 (2004)S29-49] discusses how shrinkage can be implemented within X12-ARIMA. We discuss how the seasonal factors are estimated in X12-ARIMA, how shrinkage can be translated into a moving average, if this is compatible with the philosophy behind the X12-ARIMA method, and suggest possible improvements.     

基于X-12-ARIMA模型的中国粮食消费价格运行

粮食价格与人们的实际生活成本和收入水平息息相关,甚至影响整个国民经济的发展。文章用X-12-ARIMA季节调整模型对中国1997年1月至2009年12月的粮食消费价格月度定基指数进行分解,并得到趋势循环、季节和不规则因素;通过所得异常值和趋势对我国粮食价格发展阶段进行科学划分;通过分解后的季节因素分析其季节特征,并探究它们的深层成因。结果表明：模型具有非常好的分解效果;粮价有明显的趋势和季节运行特征;粮食价格波动成因很好地解释其运行特征。文章为把握我国粮食价格运行、制定相关政策提供科学依据。     

人民币有效汇率与出口关系的实证分析

通过选取2001年1月～2005年12月的月度数据进行分析,得出长期内人民币有效汇率与我国出口之间并不存在协整关系,即从长期趋势来看,汇率变动不是我国出口的主要影响因素.     

CPI的SARIMA模型与X-12季节调整模型对比预测分析

基于SARIMA模型及X-12-ARIMA模型对我国1995年1月至2014年6月的居民消费价格指数月度数据进行预测分析。利用Eviews 6.0对CPI数据的变化趋势及季节性进行验证,结果表明X-12季节调整模型相对于SARIMA模型更有效,预测值与实际值的估计误差控制较好。     

An extended review of the X11ARIMA seasonal adjustment package

The revised version of X11ARIMA, Statistics Canada's seasonal adjustment package, is reviewed. The 1988 package, available for PCs as well as mainframes, is easy to use and more flexible than the 1980 version. New features reflecting research by Estela Dagum and her colleagues are highlighted. Comparisons are made with some other available X-11 programs.