Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/303236 
Year of Publication: 
2024
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2024-037/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
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.
Subjects: 
nowcasting
multivariate structural time series model
seemingly unrelated time series equations
Kalman filter
road fatalities
labour market statistics
JEL: 
C32
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.