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Using Survey Information for Improving the Density Nowcasting of US GDP with a Focus on Predictive Performance during Covid-19 Pandemic

Cem Çakmaklı () and Hamza Demircan ()

Koç University-TUSIAD Economic Research Forum Working Papers from Koc University-TUSIAD Economic Research Forum

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.

Keywords: Dynamic factor model; Stochastic volatility; Survey of Professional Forecasters; Disagreement; Predictive density evaluation; Bayesian inference. (search for similar items in EconPapers)
JEL-codes: C32 C38 C53 E32 E37 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2020-10
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mac and nep-ore
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