A Look into the Factor Model Black Box: Publication Lags and the Role of Hard and Soft Data in Forecasting GDP

By Marta Bańbura, Gerhard Rünstler. Social Science Research Network, 2007

351 citations recorded by the source corpus.

We derive forecast weights and uncertainty measures for assessing the roles of individual series in a dynamic factor model (DFM) for forecasting the euro area GDP from monthly indicators. The use of the Kalman smoother allows us to deal with publication lags when calculating the above measures. We find that surveys and financial data contain important information for the GDP forecasts beyond the monthly real activity measures. However, this is discovered only if their more timely publication is taken into account properly. Differences in publication lags play a very important role and should be considered in forecast evaluation.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/adea75eed6324181584285e577aa156b523413a5

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Citations in the corpus

Interconnectedness in the interbank market (2015, neutral): “ soft information, such as the Purchasing Manager Index (PMI) —Bańbura and Rünstler (2011) show that soft information may be important in forecasting);”
A two-step estimator for large approximate dynamic factor models based on Kalman filtering (2011, neutral): “The model was first implemented at the Board of Governors of the Federal Reserve and then at the European Central Bank (Angelini et al., 2008; Banbura and Rünstler, 2007; Rünstler et al., 2008; ECB, 2008).”
Factor-Midas for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP (2010, positive): “In particular, Banbura and Rünstler (2007) augment the state-space system above, see equations (5) and (6), with further relationships that interpolate GDP and relate monthly GDP to the monthly factors.”
Nowcasting GDP in Real Time: A Density Combination Approach (2014, neutral): “The idea of combining forecasts of different models was first introduced by Bates and Granger (1969). Timmermann (2006) provided an extensive survey of different combination methods.”
Surprise and Uncertainty Indexes: Real-Time Aggregation of Real-Activity Macro Surprises (2016, neutral): “The quarterly variables y t follow a similar factor model representation: y t = μ Q + Zxt + ε Q t (8) εQt = ρε Q t−1 + e Q t (9)”
Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility (2016, positive): “The results confirm the findings in Banbura and Ruenstler (2011) indicating that in the months of the quarter when the dataset is “balanced,” over half of the estimate of (unobserved) real activity growth depends on hard indicators, with soft indicators playing a role in the months when neither GDP…”