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Title: The Forcasting Performance of Dynamic Factor Models with Vintage Data

Author(s): Luca Di Bonaventura, Mario Forni and Francesco Pattarin

Publication Date: July 2018

Keyword(s): Dynamic factor models, First release data, Forecasting, Forecasting Performance and Vintage data

Programme Area(s): Monetary Economics and Fluctuations

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.

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Bibliographic Reference

Di Bonaventura, L, Forni, M and Pattarin, F. 2018. 'The Forcasting Performance of Dynamic Factor Models with Vintage Data'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=13034