Pooling versus model selection for nowcasting with many predictors: An application to German GDP
Massimiliano Marcellino and
Christian Schumacher ()
No 7197, CEPR Discussion Papers from C.E.P.R. Discussion Papers
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 different 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.
Keywords: factor models; forecast combination; forecast pooling; MIDAS; mixed-frequency data; model selection; nowcasting (search for similar items in EconPapers)
JEL-codes: C53 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-for and nep-mac
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Working Paper: Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP (2009)
Working Paper: Pooling versus model selection for nowcasting with many predictors: an application to German GDP (2009)
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