Arbeitspapier

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

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.

Language
Englisch

Bibliographic citation
Series: Working Papers of the Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour" ; No. 2

Classification
Wirtschaft
Business Fluctuations; Cycles
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
Survey Methods; Sampling Methods
Subject
Forecast error evaluation
questionnaire
survey
business cycle forecast
professional forecaster

Event
Geistige Schöpfung
(who)
Döpke, Jörg
Fritsche, Ulrich
Waldhof, Gaby
Event
Veröffentlichung
(who)
Humboldt University Berlin
(where)
Berlin
(when)
2017

DOI
doi:10.18452/18132
Handle
URN
urn:nbn:de:kobv:11-110-18452/18803-1
Last update
10.03.2025, 11:45 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Döpke, Jörg
  • Fritsche, Ulrich
  • Waldhof, Gaby
  • Humboldt University Berlin

Time of origin

  • 2017

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