Nowcasting GDP in Real-Time: A Density Combination Approach
Working paper
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http://hdl.handle.net/11250/95383Utgivelsesdato
2011Metadata
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Sammendrag
In this paper we use U.S. real-time vintage data and produce combined density nowcasts
for quarterly GDP growth from a system of three commonly used model classes. The
density nowcasts are combined in two steps. First, a wide selection of individual models
within each model class are combined separately. Then, the nowcasts from the three
model classes are combined into a single predictive density. We update the density nowcast
for every new data release throughout the quarter, and highlight the importance of
new information for the evaluation period 1990Q2-2010Q3. Our results show that the
logarithmic score of the predictive densities for U.S. GDP increase almost monotonically
as new information arrives during the quarter. While the best performing model class
is changing during the quarter, the density nowcasts from our combination framework
is always performing well both in terms of logarithmic scores and calibration tests. The
density combination approach is superior to a simple model selection strategy and also
performs better in terms of point forecast evaluation than standard point forecast combinations.
Beskrivelse
1/2010 and 2/2010 was published as CAMAR Working Paper Series (ISSN 1892-2198). From 2011 the series' name changed to CAMP Working Paper Series.