Nowcasting Norwegian household consumption with debit card transaction data
利用挪威家庭所有境内实体终端借记卡交易数据，通过混合频率数据采样模型即时预测季度家庭消费，发现借记卡数据能提升预测准确性，尤其在新冠疫情等不确定时期。
Summary 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.