A Dynamic Factor Model for Nowcasting Canadian GDP Growth

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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 mixed-data sampling (MIDAS) and bridge regressions.

DOI: https://doi.org/10.34989/swp-2017-2