Nowcasting Norwegian household consumption with debit card transaction data

Abstract

We use a novel data set covering all domestic debit card transactions in physical terminals by Norwegian households, to nowcast quarterly Norwegian household consumption. These card payments data are not subject to revisions and are available weekly without delays, providing a valuable early indicator of household spending. To account for mixed-frequency data, we estimate various quantile mixed-data sampling (QMIDAS) regressions using predictors sampled at monthly and weekly frequency. We evaluate both point and density forecasting performance over the sample 2011Q4–2019Q4. Our results show that MIDAS regressions with debit card transactions data improve both point and density forecast accuracy over competitive standard benchmark models that use alternative high-frequency predictors. Finally, we illustrate the benefits of using the card payments data by obtaining a timely and relatively accurate nowcast of 2020Q1, a quarter characterized by heightened uncertainty due to the COVID-19 pandemic. We further show how debit card data have been useful in nowcasting consumption during the four subsequent quarters.