Theories, techniques and the formation of German business cycle forecasts: Evidence from a survey among professional forecasters

Jörg Döpke, Ulrich Fritsche and Gabi Waldhof
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Jörg Döpke: University of Applied Sciences Merseburg (Hochschule Merseburg)
Gabi Waldhof: Martin-Luther-Universität Halle-Wittenberg

No 2017-002, Working Papers from The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting

Abstract: The paper reports results of a survey among active forecasters of the German business cycle. Relying on 82 respondents from 37 different institutions, we investigate what models and theories forecasters subscribe to and find that they are pronounced conservative in the sense, that they overwhelmingly rely on methods and theories that have been well-established for a long time, while more recent approaches are relatively unimportant for the practice of business cycle forecasting. DSGE models are mostly used in public institutions. In line with findings in the literature there are tendencies of “leaning towards consensus” (especially for public institutions) and “sticky adjustment of forecasts” with regard to new information. We find little evidence that the behaviour of forecasters has changed fundamentally since the Great Recession but there are signs that forecast errors are evaluated more carefully. Also, a stable relationship between preferred theories and methods and forecast accuracy cannot be established.

Keywords: Forecast error evaluation; questionnaire; survey; business cycle forecast; professional forecaster (search for similar items in EconPapers)
JEL-codes: C83 E32 E37 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2017-08
New Economics Papers: this item is included in nep-eec, nep-for and nep-mac
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Citations: View citations in EconPapers (1)

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http://www2.gwu.edu/~forcpgm/2017-002.pdf First version, 2017 (application/pdf)

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Working Paper: Theories, techniques and the formation of German business cycle forecasts. Evidence from a survey among professional forecasters (2017)
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