Comparison of Time Series Characteristics for Seasonal Adjustments from SEATS and X-12-ARIMA
Comparison of Time Series Characteristics for Seasonal Adjustments from SEATS and X-12-ARIMA
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SEATS 和 X-12-ARIMA 季节性调整的时间序列特征比较
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发表时间：
2002
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通讯作者：
C. C. Hood
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作者：
C. C. Hood
两种广泛使用的季节性调整程序是美国人口普查局的X - 12 - ARIMA以及由阿古斯丁·马拉瓦尔编写的基于ARIMA模型的信号提取程序SEATS。在之前对SEATS和X - 12 - ARIMA的研究中，我们发现一些序列，其中SEATS的调整比X - 12 - ARIMA的调整具有更小的修订量（胡德、阿什利和芬德利，2000年）。基于此前的工作，我将研究时间序列的特性，这些特性使其适合用SEATS或X - 12ARIMA进行调整。我使用了一个可以使用SEATS算法的X - 12 - ARIMA版本。这使得可以为两个程序计算类似的诊断指标——包括滑动跨度和修订诊断——以比较两个程序之间的调整。在我们早期的研究中，我们发现在我们能够推荐在统计局将SEATS用于实际工作之前，SEATS需要更多的诊断。在本文中，我举例说明为什么X - 12 - SEATS中的诊断非常有用。例如，当原始序列没有季节性时，SEATS可能会在季节性调整后的序列中引入残余季节性。X - 12 - SEATS中可用的频谱诊断对于判断原始序列是否具有季节性非常重要。我还展示了一个由于TRAMO选择的模型而导致修订量非常大的序列的例子。修订历史诊断对于发现具有大修订量的序列非常有用。
Two widely-used seasonal adjustment programs are the U.S. Census Bureau's X-12-ARIMA and the SEATS program for ARIMA-model-based signal extraction written by Agustin Maravall. In previous studies with SEATS and X-12-ARIMA, we found some series where the adjustment from SEATS had smaller revisions than the adjustment from X-12-ARIMA (Hood, Ashley, and Findley, 2000). Based on this previous work, I will investigate the properties of a time series that make it a good candidate for adjustment by SEATS or by X-12ARIMA. I used a version of X-12-ARIMA that has access to the SEATS algorithm. This allows computation of similar diagnostics for both programs — including sliding spans and revision diagnostics — to compare adjustments between the two programs. In our earlier studies, we found that SEATS needs more diagnostics before we can recommend using SEATS for production work at the Bureau. In this paper, I show examples of why the diagnostics in X-12-SEATS are very useful. For example, SEATS can induce residual seasonality into the seasonally adjusted series when the original series isn't seasonal. The spectral diagnostics availab le in X-12-SEATS are very important to be able to see if the original series is seasonal or not. I also show an example of a series with very large revisions due to the model chosen by TRAMO . The revision history diagnostics are very useful to see series with large revisions.