Deflating Bank Transaction Data for GDP Nowcasting: Whether and How to Use Inflation Lags

Bank transaction data are increasingly used to nowcast real GDP growth due to their high frequency and broad coverage. A key challenge is the choice of an appropriate price deflator to transform nominal transaction values into real terms, as transaction values reflect invoiced amounts that are observed with a delay and based on prices quoted in earlier periods. This timing mismatch complicates the use of contemporaneous inflation measures. We find that using one-quarter lagged inflation, in particular, of the GDP deflator, and of an equally weighted estimate of the first lags of price indices, consistently outperforms the benchmark model that does not adjust for inflation and models using contemporaneous inflation, across different settings and periods. At its best, the model using the one-quarter lag of the GDP deflator outperforms the benchmark in 68% of cases and achieves a maximum RMSFE reduction of 5.5%. The equally weighted prediction of models using the one-quarter lag of price indices, improves the benchmark in 54% of cases and attains a maximum RMSFE reduction of 3.78%. These findings suggest that relying on the most recent inflation data or nowcasting delayed figures like the GDP deflator may be unnecessary or even counterproductive, as lagged inflation data often offer more stable and informative signals for real-time analysis.