Predicting Finnish economic activity using firm-level data

In this paper, we compute flash estimates of Finnish monthly economic activity using firm-level data. We use a two-step procedure where the common factors extracted from the firm-level data are subsequently used as predictors in nowcasting regressions. The results show that large firm-level datasets are useful for predicting aggregate economic activity in a timely fashion. The proposed factor-based nowcasting model leads to a superior out-of-sample nowcasting performance relative to the benchmark autoregressive model, even for early nowcasts. Moreover, we find that the quarterly GDP flash estimates that we construct provide a useful real-time alternative to the current official estimates, without any substantial loss of nowcasting accuracy.