Bottom-up or direct? Forecasting German GDP in a data-rich environment
Katja Heinisch () and
Rolf Scheufele ()
Empirical Economics, 2018, vol. 54, issue 2, No 15, 705-745
Abstract In this paper, we investigate whether there are benefits in disaggregating GDP into its components when nowcasting GDP. To answer this question, we conduct a realistic out-of-sample experiment that deals with the most prominent problems in short-term forecasting: mixed frequencies, ragged-edge data, asynchronous data releases and a large set of potential information. We compare a direct leading indicator-based GDP forecast with two bottom-up procedures—that is, forecasting GDP components from the production side or from the demand side. Generally, we find that the direct forecast performs relatively well. Among the disaggregated procedures, the production side seems to be better suited than the demand side to form a disaggregated GDP nowcast.
Keywords: Contemporaneous aggregation; Nowcasting; Leading indicators; MIDAS; Forecast combination; Dynamic factor models; Forecast evaluation (search for similar items in EconPapers)
JEL-codes: E32 E37 C52 C53 (search for similar items in EconPapers)
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Working Paper: Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment (2013)
Working Paper: Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment (2012)
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