From Quarterly to Monthly Turnover Figures Using Nowcasting Methods

Zult Daan (), Krieg Sabine (), Schouten Bernd (), Ouwehand Pim () and Jan van den Brakel ()
Additional contact information
Zult Daan: Statistics Netherlands – Methodology PO Box 4000, Den Haag 2270 JM Netherlands
Krieg Sabine: Statistics Netherlands – Department of Statistical Methods PO Box 4481, Heerlen 6401 CZ Netherlands.
Schouten Bernd: Statistics Netherlands – Methodology PO Box 4000, Den Haag 2270 JM Netherlands
Ouwehand Pim: Statistics Netherlands – Methodology PO Box 4000, Den Haag 2270 JM Netherlands
Jan van den Brakel: Statistics Netherlands – Department of Statistical Methods PO Box 4481, Heerlen 6401 CZ Netherlands.

Journal of Official Statistics, 2023, vol. 39, issue 2, 253-273

Abstract: 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.

Keywords: Benchmarking; nowcasting; register statistics; mixed frequency models (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:39:y:2023:i:2:p:253-273:n:2

DOI: 10.2478/jos-2023-0012

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