GDP Forecasting using Payments Transaction Data

TL;DR

This paper explores using payment transaction data as a real-time alternative to traditional UK GDP measurement, aiming to improve timeliness and responsiveness of economic indicators.

Contribution

It introduces a novel approach of predicting GDP using payment transaction data and evaluates simple linear regression models on this data.

Findings

Low explanatory power of the regression model.

Mixed results in model reliability and residual analysis.

Potential for future research with more sophisticated methods.

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.