A mixed frequency approach to the forecasting of private consumption with ATM/POS data

Abstract

The recent worldwide development and widespread use of electronic payment systems has provided an opportunity to explore new sources of data for the monitoring of macroeconomic activity. In this paper, we analyse the usefulness of data collected from automated teller machines (ATM) and points-of-sale (POS) for nowcasting and forecasting quarterly private consumption. We take advantage of the availability of such high frequency data by using mixed data sampling (MIDAS) regressions. A comparison of several MIDAS variants proposed in the literature is conducted, and both single- and multi-variable models are considered, together with different information sets within the quarter. Given the substantial use of ATM/POS technology in Portugal, it is important to assess the information content of this data for tracking private consumption. We find that ATM/POS data display a better forecast performance than typical indicators, which reinforces the potential usefulness of this novel type of data among policymakers and practitioners.