Forecasting Indonesia's Digital Economic Growth Through QRIS Transaction Volume using the ARIMA Method with Eviews
Abstract
The development of digital technology has brought about significant changes in payment systems in Indonesia, particularly through the implementation of the Indonesian Standard Quick Response Code (QRIS). From 2022 to 2025, the use of QRIS has experienced rapid growth, as reflected in the increase in the number of users, merchants, and transaction intensity. This situation requires in-depth analysis to project future transaction volumes. This study aims to analyse historical patterns and forecast QRIS transaction volumes using the Autoregressive Integrated Moving Average (ARIMA) method with quarterly data from 2022Q1 to 2025Q2. Following the Box–Jenkins approach, the study includes stationarity testing, model identification, estimation, diagnostic testing, and forecasting. The ADF test results show that the data becomes stationary at second-order differencing (d=2). Of the various models, ARIMA(0,2,1) was selected as the best model based on the lowest AIC and SIC values and the highest adjusted R-squared, as well as meeting all diagnostic tests. The forecasting results show a strong upward trend in QRIS transaction volumes until 2027, with projections reaching 4,779 trillion rupiah. These findings confirm the potential of QRIS digitalisation in driving financial inclusion, empowering MSMEs, and accelerating Indonesia's economic growth.
Keywords: QRIS, Economic Growth ,Time Series Analysis ,Forecasting, ARIMA model
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