Nowcasting with Dynamic Factor Model and Real-Time Vintage Data: A financial market actor's perspective

Sammanfattning: We develop and examine a dynamic factor nowcasting model (DFM) from the perspective of a financial market participant. The first point of analysis is the examination of its performance. Unlike other papers, we evaluate with daily frequency so that the performance metric reflects a continuous nowcasting signal. Secondly, we examine the effect of using real-time vintage data which avoids look-ahead bias, compared to the common practice pseudo real-time vintage data. We conclude that the DFM outperforms a simple benchmark AR model and an alternative factor model approach. However, it is unsuccessful in incorporating newly released information to improve its estimate in the second half of a quarter. We also conclude that using pseudo real-time data sets may be misleading during times of high volatility due to economic variables being revised, or when using a DFM of less than 4 factors. In general, however, the differences in performance between the data approaches are small when modeling with a DFM, indicating that using pseudo data sets is a reasonable approach to tackle the issue of short supply of vintage data.