Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/27657
Authors: 
Kuzin, Vladimir N.
Marcellino, Massimiliano
Schumacher, Christian
Year of Publication: 
2009
Series/Report no.: 
Discussion paper Series 1 / Volkswirtschaftliches Forschungszentrum der Deutschen Bundesbank 2009,03
Abstract: 
This paper discusses pooling versus model selection for now- and forecasting in the presence of model uncertainty with large, unbalanced datasets. Empirically, unbalanced data is pervasive in economics and typically due to di¤erent sampling frequencies and publication delays. Two model classes suited in this context are factor models based on large datasets and mixed-data sampling (MIDAS) regressions with few predictors. The specification of these models requires several choices related to, amongst others, the factor estimation method and the number of factors, lag length and indicator selection. Thus, there are many sources of mis-specification when selecting a particular model, and an alternative could be pooling over a large set of models with different specifications. We evaluate the relative performance of pooling and model selection for now- and forecasting quarterly German GDP, a key macroeconomic indicator for the largest country in the euro area, with a large set of about one hundred monthly indicators. Our empirical findings provide strong support for pooling over many specifications rather than selecting a specific model.
Subjects: 
casting
forecast combination
forecast pooling
model selection
mixed - frequency data
factor models
MIDAS
JEL: 
C53
E37
Document Type: 
Working Paper

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