Statistical Early Warning Models with Applications

Lucas P. Harlaar, Jacques J.F. Commandeur, Jan A. van den Brakel, Siem Jan Koopman, Niels Bos and Frits D. Bijleveld
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Lucas P. Harlaar: Vrije Universiteit Amsterdam
Jacques J.F. Commandeur: Vrije Universiteit Amsterdam
Jan A. van den Brakel: Maastricht University
Niels Bos: SWOV Institute for Road Safety Research
Frits D. Bijleveld: Vrije Universiteit Amsterdam

No 24-037/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: This paper investigates the feasibility of using earlier provisional data to improve the now- and forecasting accuracy of final and official statistics. We propose the use of a multivariate structural time series model which includes common trends and seasonal components to combine official statistics series with related auxiliary series. In this way, more precise and more timely nowcasts and forecasts of the official statistics can be obtained by exploiting the higher frequency and/or the more timely availability of the auxiliary series. The proposed method can be applied to different data sources consisting of any number of missing observations both at the beginning and at the end of the series simultaneously. Two empirical applications are presented. The first one focuses on fatal traffic accidents and the second one on labour force participation at the municipal level. The results demonstrate the effectiveness of our proposed approach in improving forecasting performance for the target series and providing early warnings to policy-makers.

Keywords: nowcasting; multivariate structural time series model; seemingly unrelated time series equations; Kalman filter; road fatalities; labour market statistics (search for similar items in EconPapers)
JEL-codes: C32 (search for similar items in EconPapers)
Date: 2024-05-30
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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