Working Paper: CEPR ID: DP13034
Authors: Luca Di Bonaventura; Mario Forni; Francesco Pattarin
Abstract: We present a comparative analysis of the forecasting performance of two dynamic factor models, the Stock and Watson (2002a, b) model and the Forni, Hallin, Lippi and Reichlin (2005) model, based on vintage data. Our dataset contains 107 monthly US “first release” macroeconomic and financial vintage time series, spanning the 1996:12 to 2017:6 period with monthly periodicity, extracted from the Bloomberg database†. We compute real-time one-month-ahead forecasts with both models for four key macroeconomic variables: the month-on-month change in industrial production, the unemployment rate, the core consumer price index and the ISM Purchasing Managers’ Index. First, we find that both the Stock and Watson and the Forni, Hallin, Lippi and Reichlin models outperform simple autoregressions for industrial production, unemployment rate and consumer prices, but that only the first model does so for the PMI. Second, we find that neither models always outperform the other. While Forni, Hallin, Lippi and Reichlin’s beats Stock and Watson’s in forecasting industrial production and consumer prices, the opposite happens for the unemployment rate and the PMI.
Keywords: Dynamic Factor Models; Forecasting; Forecasting Performance; Vintage Data; First Release Data
JEL Codes: C01; C32; C52; C53; E27; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| SW model (C59) | Forecasting accuracy for industrial production (C53) |
| FHLR model (C59) | Forecasting accuracy for industrial production (C53) |
| SW model (C59) | Forecasting accuracy for unemployment rate (E27) |
| FHLR model (C59) | Forecasting accuracy for consumer prices (E37) |
| SW model (C59) | Forecasting accuracy for PMI (C53) |
| SW model (C59) | Forecasting accuracy for consumer prices (E37) |
| SW model (C59) | FHLR model forecasting accuracy (C53) |