Do Pix Transactions Improve Monthly Nowcasts of Brazilian Economic Activity? Evidence That Seasonal Control Absorbs the Signal

This paper investigates whether transaction data from Pix, Brazil’s instant payment system, improve the monthly nowcasting of Brazilian economic activity, measured by the Central Bank Economic Activity Index (IBC-Br). Pix data are released within days of each month’s end, well before the IBC-Br, which gives them a potential timing advantage. Using monthly Central Bank data from January 2021 to May 2026, we estimate first-difference predictive regressions relating the IBC-Br to its own lagged change, the Selic policy rate, and Pix transaction growth, under four seasonal treatments: no seasonal controls, quarter-of-year dummies, month-of-year dummies, and the officially seasonally adjusted IBC-Br. Out-of-sample accuracy is evaluated with expanding windows of three minimum lengths, Diebold–Mariano and Clark–West tests, and bootstrap critical values that reproduce the small-sample estimation noise of the early windows. Pix growth is strongly associated with the IBC-Br, and improves forecasts, only in specifications that leave month-of-year seasonality uncontrolled. Once monthly seasonality is controlled, Pix adds no forecasting gain and its coefficient is small and statistically insignificant, while the seasonally controlled models forecast the IBC-Br considerably more accurately than any specification without them. With the officially adjusted series, the association has the opposite sign to an activity signal. A recursive model-selection rule never selects a specification with Pix, and the model-averaged effect is economically small. These conclusions are robust to longer estimation windows, to controls external to the IBC-Br, and to simulated data revisions. Aggregate Pix volume is therefore not, at the current sample length, a useful monthly nowcasting input once its calendar component is removed. The paper also documents an aggregation pitfall in the Pix open-data interface and provides replication code.