Using National Payment System Data to Nowcast Economic Activity in Azerbaijan

Ilkin Huseynov (), Nazrin Ramazanova and Hikmat Valirzayev ()
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Ilkin Huseynov: Central Bank of Azerbaijan Republic
Hikmat Valirzayev: Central Bank of Azerbaijan Republic

No 2201, Working Papers from Central Bank of Azerbaijan Republic

Abstract: This study examines whether payment systems data can be useful in tracking economic activity in Azerbaijan. We utilize transactional payment systems data at the sectoral level and we employ Dynamic Factor Model (DFM) and Machine Learning (ML) techniques to nowcast quarter-over-quarter and year-over-year nominal GDP. We compare nowcasting performance of these models against the benchmark model in terms of out-of-sample RMSE at three different horizons during the quarter. The results suggest that payment system data along with ML and DFM models have higher predictability than benchmark model and can lower nowcast errors significantly. Although our payment time series are still too short to obtain statistically robust results, the findings indicate that variables at a higher frequency such payments systems data can be helpful to assess the current state of the economy and have the potential to provide a faster estimate of the economic activity.

Keywords: payment data; nowcasting; ML; DFM (search for similar items in EconPapers)
JEL-codes: C32 C38 C52 C53 E42 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2022-05-28
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Working Paper: Using National Payment System Data to Nowcast Economic Activity in Azerbaijan (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:aze:wpaper:2201

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