Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2024-05-30
Number: 24-037/III
Author-Name: Lucas P. Harlaar
Author-Workplace-Name: Vrije Universiteit Amsterdam
Author-Name: Jacques J.F. Commandeur
Author-Workplace-Name: Vrije Universiteit Amsterdam
Author-Name: Jan A. van den Brakel
Author-Workplace-Name: Maastricht University
Author-Name: Siem Jan Koopman
Author-Workplace-Name: Vrije Universiteit Amsterdam
Author-Name: Niels Bos
Author-Workplace-Name: SWOV Institute for Road Safety Research
Author-Name: Frits D. Bijleveld
Author-Workplace-Name: Vrije Universiteit Amsterdam
Title: Statistical Early Warning Models with Applications
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.
Classification-JEL: C32
Keywords: nowcasting, multivariate structural time series model, seemingly unrelated time series equations, Kalman filter, road fatalities, labour market statistics
File-URL: https://papers.tinbergen.nl/24037.pdf
File-Format: application/pdf
File-Size: 1.681.494 bytes
Handle: RePEc:tin:wpaper:20240037