GDP Forecasting using Payments Transaction Data

Arunav Das

Papers from arXiv.org

Abstract: UK GDP data is published with a lag time of more than a month and it is often adjusted for prior periods. This paper contemplates breaking away from the historic GDP measure to a more dynamic method using Bank Account, Cheque and Credit Card payment transactions as possible predictors for faster and real time measure of GDP value. Historic timeseries data available from various public domain for various payment types, values, volume and nominal UK GDP was used for this analysis. Low Value Payments was selected for simple Ordinary Least Square Simple Linear Regression with mixed results around explanatory power of the model and reliability measured through residuals distribution and variance. Future research could potentially expand this work using datasets split by period of economic shocks to further test the OLS method or explore one of General Least Square method or an autoregression on GDP timeseries itself.

Date: 2021-01
New Economics Papers: this item is included in nep-big and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2101.06478 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2101.06478

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().