A dynamic factor model for nowcasting Canadian GDP growth

This paper estimates a dynamic factor model (DFM) for nowcasting Canadian gross domestic product. The model is estimated with a mix of soft and hard indicators, and it features a high share of international data. The model is then used to generate nowcasts, predictions of the recent past and current state of the economy. In a pseudo-real-time setting, we show that the DFM outperforms univariate benchmarks, as well as other commonly used nowcasting models, such as MIDAS and bridge regressions.