Forecasting Professional Forecasters

By Eric Ghysels, Jonathan H. Wright. 2009

154 citations recorded by the source corpus.

Surveys of forecasters, containing respondents’ predictions of future values of key macroeconomic variables, receive a lot of attention in the financial press, from investors and from policy makers. They are apparently widely perceived to provide useful information about agents’ expectations. Nonetheless, these survey forecasts suffer from the crucial disadvantage that they are often quite stale, as they are released only infrequently. In this article, we propose MIDAS regression and Kalman filter methods for using asset price data to construct daily forecasts of upcoming survey releases. Our methods also allow us to predict actual outcomes, providing competing forecasts, and allow us to estimate what professional forecasters would predict if they were asked to make a forecast each day, making it possible to measure the effects of events and news announcements on expectations.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/996a9ef8b85950b452a1a5628957b794e37675c5

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Citations in the corpus

Do High-Frequency Financial Data Help Forecast Oil Prices? The Midas Touch at Work (2015, background): “…MIDAS regressions to improve the accuracy of quarterly macroeconomic forecasts based on monthly predictors and the accuracy of monthly forecasts based on daily or weekly predictors (e.g., Andreou, Ghysels, and Kourtellos 2013; Clements and Galvao 2008, 2009; Ghysels and Wright 2009; Hamilton 2008).”
State Space Models and MIDAS Regressions (2013, methodology): “The mismatch of sampling frequency has been addressed in the context of state space models by Harvey and Pierse (1984), Harvey (1989), Bernanke, Gertler, and Watson (1997), Zadrozny (1990), Mariano and Murasawa (2003), Mittnik and Zadrozny (2004), Aruoba, Diebold, and Scotti (2009), Ghysels and Wrig”
Now-Casting and the Real-time Data Flow (2013, unknown): “For example Ghysels and Wright (2009) construct news using market expectations.”
Nowcasting GDP in Real Time: A Density Combination Approach (2014, unknown): “Lately, increased interest has also been given to mixed data sampling (MIDAS) models (see, among others, Clements and Galvão 2008, 2009; Ghysels and Wright 2009; Kuzin, Marcellino, and Schumacher 2011).”
A Survey of Econometric Methods for Mixed-Frequency Data (2013, methodology): “Among these studies, Ghysels and Wright (2009) propose methods for using asset price data to construct daily forecasts of upcoming survey releases, employing MIDAS regression models and a more structural approach based on the Kalman filter to estimate what forecasters would predict if they were…”
Macroeconomics and the Reality of Mixed Frequency Data (2016, background): “…(1984), Bernanke et al. (1997), Zadrozny (1990), Mariano and Murasawa (2003), Mittnik and Zadrozny (2004), and more recently Aruoba et al. (2009), Ghysels and Wright (2009), Kuzin et al. (2009), Marcellino and Schumacher (2010), Foroni and Marcellino (2014) and Schorfheide and Song (2013), among…”
Forecasting with Mixed Frequencies (2010, background): “Ghysels and Wright (2009) included changes in daily interest rates in a MIDAS model to predict upcoming quarterly releases from the Survey of Professional Fore casters.”
Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models (2019, background): “We consider three commonly-used weight functions (Ghysels, 2009), i.e., (1) Equally-weighted forecast: ωj, th = 1/M, (2) AIC-weighted forecast: ωhj;t ¼ e −ðAIC j Þ XM j¼1 e−ðAIC jÞ , and (3) BIC-weighted forecast: ωhj;t ¼ e −ðBIC j Þ XM j¼1 e−ðBIC jÞ .”