The Use and Abuse of Real-Time Data in Economic Forecasting

By Evan F. Koenig, Sheila Dolmas, Jeremy Piger. Review of Economics and Statistics, 2000

186 citations recorded by the source corpus.

We distinguish between three different strategies for estimating forecasting equations with real-time data and argue that the most popular approach should generally be avoided. The point is illustrated with a model that uses current-quarter monthly industrial production, employment, and retail sales data to predict real GDP growth. When the model is estimated using either of our two alternative methods, its out-of-sample forecasting performance is superior to that obtained using conventional estimation and compares favorably with that of the Blue Chip consensus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/6ed24c6333ef77153bb5f40bbf61ae05102c31e6

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Citations in the corpus

Macroeconomic Forecasting With Mixed-Frequency Data (2008, methodology): “We also evaluated the suggestion by Koenig et al. (2003) to base the real-time forecasting exercise on real-time vintage data as opposed to end-of-sample vintage data.”
Nowcasting: the real time informational content of macroeconomic data releases (2008, background): “…have shown that these models are successful in this regard (Boivin and Ng, 2005; Forni et al., 2005b; D’Agostino and Giannone, 2006; Giannone et al., 2004; Marcellino et al., 2003; Stock and Watson 2002a, b), but FMs have not been used specifically for the problem of nowcasting in real time.”
Exchange Rates and Fundamentals: New Evidence from Real-Time Data (2005, methodology): “vintage data as is common when estimating Taylor rules. Koenig et al. (2000) show that”
AN INDICATOR OF MONTHLY GDP AND AN EARLY ESTIMATE OF QUARTERLY GDP GROWTH (2005, unknown): “Koenig et al. (2003) come to a similar conclusion for the US.”
Real-Time Forecasts of the Real Price of Oil (2012, background): “The problem is that the underlying time series are not stationary because they include data that have been revised to different degrees (e.g., see Koenig, Dolmas, and Piger 2003; Clements and Galvão 2010; Croushore 2011).”