Nowcasting consists of tracking GDP by focusing on the data flow consisting oftimely available releases. The essential features of a nowcasting data set are differing sampling frequencies, and the ragged-edge: the differing patterns of missing observations at the end of the sample due to non-synchronicity of data publications. Various econometric frameworks have been proposed to deal withthese characteristics. During a sequence of subsequent nowcast occasions, the models are traditionally re-estimated, or updated, based on an expanding dataset as more and more data becomes available. This paper proposes to take the ragged-edge structure into account when estimating a nowcast model. Instead of using all available historical data, it is here proposed to first mask the historical data so as to reflect the pattern of data availability at the specific nowcast occasion. Since each nowcast occasion exhibits its own specific ragged-edge structure, we propose to re-estimate or recalibrate the model at each juncture employing the accompanying mask, hence dynamic masking.Dynamic masking thus tailors the model to the specific nowcast occasion. It is shown how tailoring improves precision by employing ridge regressions with and without masking in a real-time nowcasting back-test.
The masking approach is motivated by theory and demonstrated on real data. It surpasses the dynamic factor model in backtests. Dynamic masking gives ease of implementation, a solid theoretical foundation, flexibility in modeling, and encouraging results; we therefore consider it a relevant addition to the nowcasting methodology.
Nowcasting; Time-series; Masking; Ragged edge; Machine Learning.