Short-term business statistics at Statistics Netherlands are largely based on Value Added Tax (VAT) administrations. Companies may decide to file their tax return on a monthly, quarterly, or annual basis. Most companies file their tax return quarterly. So far, these VAT based short-term business statistics are published with a quarterly frequency as well. In this article we compare different methods to compile monthly figures, even though a major part of these data is observed quarterly. The methods considered to produce a monthly indicator must address two issues. The first issue is to combine a high- and low-frequency series into a single high-frequency series, while both series measure the same phenomenon of the target population. The appropriate method that is designed for this purpose is usually referred to as “benchmarking”. The second issue is a missing data problem, because the first and second month of a quarter are published before the corresponding quarterly data is available. A “nowcast” method can be used to estimate these months. The literature on mixed frequency models provides solutions for both problems, sometimes by dealing with them simultaneously. In this article we combine different benchmarking and nowcasting models and evaluate combinations. Our evaluation distinguishes between relatively stable periods and periods during and after a crisis because different approaches might be optimal under these two conditions. We find that during stable periods the so-called Bridge models perform slightly better than the alternatives considered. Until about fifteen months after a crisis, the models that rely heavier on historic patterns such as the Bridge, MIDAS and structural time series models are outperformed by more straightforward (S)ARIMA approaches.

中文翻译：

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使用临近预报方法从季度到月度营业额数据

荷兰统计局的短期商业统计主要基于增值税 (VAT) 管理。公司可以决定每月、每季度或每年提交纳税申报表。大多数公司每季度提交一次纳税申报表。到目前为止，这些基于增值税的短期业务统计数据也是每季度发布一次。在本文中，我们比较了不同的方法来编制月度数据，尽管这些数据的主要部分是按季度观察的。考虑产生月度指标的方法必须解决两个问题。第一个问题是将高频和低频序列组合成一个高频序列，同时两个序列测量目标人群的相同现象。为此目的而设计的适当方法通常称为“基准测试”。第二个问题是数据缺失问题，因为一个季度的第一个月和第二个月在相应的季度数据可用之前就已经发布了。可以使用“临近预报”方法来估计这些月份。关于混合频率模型的文献为这两个问题提供了解决方案，有时是同时处理它们。在本文中，我们结合了不同的基准测试和临近预报模型并评估组合。我们的评估区分了相对稳定的时期以及危机期间和之后的时期，因为在这两种情况下不同的方法可能是最佳的。我们发现，在稳定时期，所谓的 Bridge 模型的性能略好于所考虑的替代方案。直到危机发生大约 15 个月后，那些更依赖历史模式的模型，例如 Bridge，