The recent shutdown of significant portions of the worldwide economy, in order to restrain the outbreak of the coronavirus, has triggered a global recession. The uncertain consequences of the rapid spread of the virus and the induced infection control measures have made it extremely challenging for forecasters and policymakers to quantify and assess the current and future outlook of the economy. This has raised a renewed interest in the search for reliable high-frequency indicators that can track the real economy in a timely matter. In this paper, we document that debit card transaction data serve as an early and reliable indicator for household consumption in Norway. We use a novel data set covering all debit card transactions for Norwegian households to nowcast quarterly household consumption in Norway. These card payments data are free of sampling errors and are available without delays, and currently account for more than 35% of the total value of all household consumption expenditures. Therefore, they providing a valuable early indicator of household spending. To account for mixed-frequency data, we estimate various mixed-data sampling (MIDAS) regressions using predictors sampled at monthly and weekly frequency. We evaluate both point and density forecasting performance over the sample 2011Q4-2020Q1. Our results show that MIDAS regressions with credit card transaction 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 the first quarter of 2020, a quarter characterized by heightened uncertainty due to the Covid-19 pandemic.<\/p>\n
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