Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/243000
Authors: 
Çakmaklı, Cem
Demircan, Hamza
Year of Publication: 
2020
Series/Report no.: 
Working Paper No. 2016
Publisher: 
Koç University-TÜSIAD Economic Research Forum (ERF), Istanbul
Abstract: 
We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of US real GDP. Specifically, we use the conventional dynamic factor model together with a stochastic volatility component as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants' predictions, often used as a measure of 'ambiguity', conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution.
Subjects: 
Dynamic factor model
Stochastic volatility
Survey of Professional Forecasters
Disagreement
Predictive density evaluation
Bayesian inference
JEL: 
C32
C38
C53
E32
E37
Document Type: 
Working Paper

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