基于匹配性的社会消费品零售总额数据质量评估研究

文章在数据质量评估与管理文献梳理的基础上,从社会消费品零售总额数据质量内涵出发,分析基于匹配性的社会消费品零售总额的数据质量评估要素;基于社会消费品零售总额自身数据特点和城镇家庭居民人均可支配收入相匹配的核心要素,构建自回归动态滞后模型;采集2002年第一季度至2011年第一季度的数据,从匹配性的角度,利用构建的模型对社会消费品零售总额进行数据质量评估。评估结果发现,中国社会消费品零售总额数据误差总体在允许的5%范围以内,但误差的变动在近年有扩大趋势。     

基于VAR模型的深圳GDP增长的影响因素分析

文章对1979～2006年深圳的GDP与进口额、工业增加值、出口额和社会消费品零售总额的有关统计数据进行基于VAR模型的实证分析.分析结果表明:长期看,工业增加值、出口额和社会消费品零售总额的增加导致了深圳的经济增长,而进口额的增加,则使深圳的生产总值减少,并且影响程度从高到低依次为社会消费品零售总额、进口额、出口额与工业增加值;短期看,影响深圳的经济增长的影响程度从高到低依次为进口额,出口额、工业增加值及社会消费品零售总额,这结果符合经济规律,可为深圳制订经济发展目标提供决策参考.     

浦东新区社会消费品零售总额数据质量评估

社会消费品零售总额指标是反映一定时期内通过各种商品流通渠道向城乡居民和社会集团供应的消费品总量,是研究消费品零售市场容量及发展趋势、分析居民的消费品购买能力实现情况、了解居民和社会集团消费水平和结构变化的重要指标.因此,社会消费品零售总额数据质量直接影响到正确研究消费品零售市场供求平稳状况、流通领域有关政策的合理制定.为此,对社会消费品零售总额数据质量进行分析有一定现实意义.     

完善社会消费品零售总额统计指标之我见

现行社会消费品零售总额统计指标包括两部份，一是社会消费品零售额，它是指对居民和社会集团的消费品零售; 另一部份是农民对非农业居民零售额。农民之间消费品零售（这里是指生活消费品、下同），该不该统计在社会消费品零售总额中呢？笔者认为，把农民之间消费品零售统计在社会消费品零售总额中，是科学的、合适的，是完善了社会消费品零售总额统计指标的范围和内容。农民之间消费品零售应是社会消费品零售总额的组成部分为了进一步说明农民之间消费品零售是社会消费品零售总额的组成部分，现结合商品流转的概念及分类来阐述。商品流转是…     

中国社会消费品零售总额的预测分析

文章以我国1990 ～ 2010年的社会消费品零售总额的数据为样本建立指数平滑法模型,并利用该模型对2011～2013年进行预测分析,通过分析发现该模型预测误差很小,拟合效果能达到预期的目的.预测结果表明2011 ～ 2013年我国社会消费品零售总额总体上将持续增长,这反映了消费品市场在快速发展中的连续性和稳定性,可以为政府的宏观决策提供依据.     

社会消费品零售总额实际预测方法的探讨

社会消费品零售总额是反映通过各种商品流通渠道向居民和社会集团供应的生活消费品来满足他们生活需要,是研究人民生活、社会消费品购买力、货币流通等问题的重要指标.从长期趋势的时间序列看,社会消费品零售总额及其增长率变动有其内在的规律性.本文依据最近十年来我市社会消费品零售总额的历史资料,运用不同的数学模型和经济预测方法,在各种预测方法中找出最佳的社会消费品零售总额预测方法.     

浙江社会消费品零售总额增长因素分析

近年来,浙江的社会消费品零售总额、城镇居民可支配收入总额等指标呈逐年增长态势。通过对浙江社会消费品零售总额与人口和城乡结构、价格变动因素等的相关分析,并对其预测模型的构建,得出社会消费品零售总额对浙江GDP增长影响显著;社会消费品零售总额增长与人口增长、城乡结构变化、价格变动因素等高度相关等结论。     

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。     

上海社会消费品零售总额的灰色系统分析

社会消费品零售总额指国民经济各行业直接售给城乡居民和社会集团的消费品总额.它是反映各行业通过多种商品流通渠道向居民和社会集团供应的生活消费品总量,是研究国内或地区零售市场变动情况、反映经济景气程度的重要指标.社会消费品零售总额由吃、穿、用、烧等四项社会消费构成.而全社会消费是通过消费品市场得到最终实现的.因此,分析上海社会消费品零售总额及其构成,以及研究对消费品零售总额产生影响的主要因素,对总体地研究上海消费品市场的现状和发展有着重要意义.     

基于误差修正模型分析内蒙古社会消费品零售总额与生产总值的关系

从宏观经济的角度考虑,生产总值和社会消费品零售总额之间存在着密切的关系.虽然经济学家对二者之间进行了较为细致的研究,但是由于经济体的差异性导致二者之间的关系不尽相同,尤其是社会消费在不同的gdp有着不同的特征.同时,不同学者所采用的分析方法、模型、数据以及数据处理方法各不相同,对于影响社会消费品零售总额的gdp影响因素关系分析也不相同.经济体在时间上对上一时期消费偏离程度、弹性大小等具体影响情况随着经济体的快速增长而不断变化.对内蒙古自治区而言,随着迅速增加的国民生产总值,其社会消费品零售总额与国民生产总值之间往往用一般的模型难以十分准确的描述二者之间的关系.     

支持向量回归特征提取的ARMA准则——中国社会消费品零售总额预测的实证研究

根据统计学习理论,针对局部灰色支持向量回归方法,提出了单变量经济时间序列预测特征提取的ARMA准则.对中国社会消费品零售总额的试验结果表明:ARMA准则能客观准确地实现特征提取,获得较高的预测精度.     

Forecasting Economic Time Series With Structural and Box-Jenkins Models: A Case Study

The basic structural model is a univariate time series model consisting of a slowly changing trend component, a slowly changing seasonal component, and a random irregular component. It is part of a class of models that have a number of advantages over the seasonal ARIMA models adopted by Box and Jenkins (1976). This article reports the results of an exercise in which the basic structural model was estimated for six U.K. macroeconomic time series and the forecasting performance compared with that of ARIMA models previously fitted by Prothero and Wallis (1976).     

Bayesian Forecasting of Many Count-Valued Time Series

Abstract

We develop and exemplify application of new classes of dynamic models for time series of nonnegative counts. Our novel univariate models combine dynamic generalized linear models for binary and conditionally Poisson time series, with dynamic random effects for over-dispersion. These models estimate dynamic regression coefficients in both binary and nonzero count components. Sequential Bayesian analysis allows fast, parallel analysis of sets of decoupled time series. New multivariate models then enable information sharing in contexts when data at a more highly aggregated level provide more incisive inferences on shared patterns such as trends and seasonality. A novel multiscale approach—one new example of the concept of decouple/recouple in time series—enables information sharing across series. This incorporates cross-series linkages while insulating parallel estimation of univariate models, and hence enables scalability in the number of series. The major motivating context is supermarket sales forecasting. Detailed examples drawn from a case study in multistep forecasting of sales of a number of related items showcase forecasting of multiple series, with discussion of forecast accuracy metrics, comparisons with existing methods, and broader questions of probabilistic forecast assessment.     

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.     

An Application of Nonlinear Time Series Forecasting

By means of a real application, it is seen how ARIMA forecasts can be improved when nonlinearities are present. The autocorrelation function (ACF) of the squared residuals provides a convenient tool to check the linearity assumption. Once nonlinearity has been detected, parsimonious bilinear processes seem rather adequate to model it. The detection of nonlinearity and the forecast improvement appear to be rather robust with respect to changes in the linear and bilinear specification. Finally, what bilinear models seem to capture are periods of atypical behavior or sequences of outliers.     

Census data quality--a user's view

"This paper presents the perspective of a major user of both decennial and economic [U.S.] census data. It illustrates how these data are used as a framework for commercial marketing research surveys that measure television audiences and sales of consumer goods through retail stores, drawing on Nielsen's own experience in data collection and evaluation. It reviews Nielsen's analyses of census data quality based, in part, on actual field evaluation of census results. Finally, it suggests ways that data quality might be evaluated and improved to enhance the usefulness of these census programs."     

A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series

The actual performance of several automated univariate autoregressive forecasting procedures, applied to 150 macroeconomic time series, are compared. The procedures are the random walk model as a basis for comparison; long autoregressions, with three alternative rules for lag length selection; and a long autoregression estimated by minimizing the sum of absolute deviations. The sensitivity of each procedure to preliminary transformations, data, periodicity, forecast horizon, loss function employed in parameter estimation, and seasonal adjustment procedures is examined. The more important conclusions are that Akaike's lag-length selection criterion works well in a wide variety of situations, the modeling of long memory components becomes important for forecast horizons of three or more periods, and linear combinations of forecasts do not improve forecast quality appreciably.     

Discussion: The Recent Reappearance of the Homotheticity Restriction on Preferences

A model linking final demand (sales) with intermediate manufactures is estimated and simulated over the course of a business cycle. The results show that prices fluctuate more for early-stage manufactures than for later-stage products at the manufacturing and retail level. This is found to be largely due to inventory and unfilled orders fluctuations, particularly those of inventories of primary manufactured products held by finished goods manufacturers. Since these inventory fluctuations lead the cycle in final demand, prices of primary manufactures lead business activity. Another result is that inventories of materials and supplies, including those of retailers for resale, exhibit more instability than producers' inventories of finished goods.