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AI 根据所提供的论文材料归类:摘要直接研究经济时间序列的季节成分、季节调整及模型敏感性。

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关注这篇论文的作者
J. B. Carlin
A. P. Dempster

季节调整的敏感性分析:实证案例研究

研究问题
经济时间序列的季节调整结果会在多大程度上依赖合理的模型选择,以及固定拟合参数是否遗漏重要不确定性?
方法与思路
作者建立季节与非季节成分模型,在半贝叶斯框架下计算条件后验,并比较多个合理拟合模型的调整结果。
材料陈述的贡献
摘要称,三个案例表明模型敏感性在实践中并不少见;作者据此主张对参数后验进行平均,并讨论先验依赖。
阅读关注点
应核对成分模型、固定与随机效应和交易日调整的设定,以及模型敏感性如何评估、与 X-11 等方法如何比较。
阅读边界
仅据 OpenAlex 索引摘要,稿件版本未注明,不能推断刊本变化;摘要说明文章省略部分算法细节,案例差异也不构成某种方法普遍优越的证明。

原文摘要

Three detailed case studies illustrating the seasonal analysis of economic time series are presented using component models for seasonal and nonseasonal behavior. Analyses are performed within a semi-Bayesian framework where inferences for target quantities of interest, such as seasonally adjusted values, are obtained as posterior distributions conditional on observed data and fitted parameter values. Such an approach is similar to previous model-based methods of seasonal analysis, but new models and algorithms are used and, more important, a sensitivity analysis is performed to determine the extent to which conclusions vary across a range of plausible fitted models. It is found that sensitivity to variation across plausible models is not unusual in practice. The logical conclusion of the investigation is that a fully Bayesian analysis is required that averages conditional posteriors over a posterior distribution for the model parameters. Such an analysis is necessarily sensitive to the choice of prior distribution. We believe that such dependencies on assumptions external to the data are inevitable in complex problems such as seasonal adjustment. The stochastic components of our nonseasonal and seasonal models are based on modified fractional Gaussian noise, as described in Carlin, Dempster, and Jonas (1985). The models allow for joint estimation of both fixed and random effects, a capability that is used to estimate the initial values of nonstationary components and is further illustrated in the data analysis by the use of trading-day adjustments. Our model structure is described in detail, but technical details of algorithms used to perform likelihood and conditional posterior calculations for fitted models are omitted in favor of the empirical case studies. The examples include some comparisons to the nonparametric X-11 method and to an autoregressive moving average modeling approach. One of the examples exhibits particularly striking differences between the model-based seasonal adjustments and those of X-11.

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