Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/278195 
Year of Publication: 
2022
Series/Report no.: 
Graduate Institute of International and Development Studies Working Paper No. HEIDWP23-2022
Publisher: 
Graduate Institute of International and Development Studies, Geneva
Abstract: 
This study examines whether payment system data can be useful for tracking economic activity in Azerbaijan. We utilise the transactional payment system data at the sectoral level and employ a Dynamic Factor Model (DFM) and Machine Learning (ML) techniques to nowcast quarterover-quarter and year-over-year nominal gross domestic product. We compared the nowcasting performance of these models against the benchmark model in terms of the out-of-sample root mean square error at three different horizons during the quarter. The results suggest that ML and DFM models have higher predictability than the benchmark model and can significantly lower nowcast errors. Although our payment time series is still too short to obtain statistically robust results, the findings indicate that variables at a higher frequency in such data can be helpful in assessing the current state of the economy and have the potential to provide a faster estimate of the economic activity.
Subjects: 
payment data
nowcasting
ML
DFM
JEL: 
C32
C38
C52
C53
E42
Document Type: 
Working Paper

Files in This Item:
File
Size





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