Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/201261
Authors: 
Fornaro, Paolo
Luomaranta, Henri
Saarinen, Lauri
Year of Publication: 
2017
Series/Report no.: 
ETLA Working Papers 46
Abstract: 
We adopt a series of shrinkage and factor analytic methodologies to compute nowcasts of the main Finnish turnover indexes, using continuously accumulating firm-level data. We show that the estimates based on large dimensional models provide an accurate and timelier alternative to the ones produced currently by Statistics Finland, even after taking into account data revisions. In particular, we find that the turnovers for the service sector can be estimated with high accuracy five days after the reference month has ended, giving more accurate and faster predictions compared to the first official internal release. For other sectors, the large dimensional models provide a good nowcasting performance, even though there is a timeliness-accuracy trade off. Finally, we propose a factor-based methodology to improve the accuracy of the current flash estimates by imputing part of the data sources, and find that we are able to provide better predictions in a more expedited fashion for all sectors of interest.
Subjects: 
Dynamic factor models
Firm-level data
Nowcasting
Shrinkage
JEL: 
C31
C53
C55
Document Type: 
Working Paper

Files in This Item:
File
Size





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