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Theories, Techniques and the Formation of German Business Cycle Forecasts

Evidence from a survey of professional forecasters
  • Jörg Döpke , Ulrich Fritsche EMAIL logo und Gabi Waldhof
Veröffentlicht/Copyright: 12. April 2019

Abstract

We report results of a survey among active forecasters of the German business cycle. Using data for 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. A stable relationship between preferred theories and methods and forecast accuracy cannot be established.

JEL Classification: E32; E37; C83

Acknowledgements

We thank three anonymous referees, Ullrich Heilemann, Christian Pierdzioch, Christian Breuer, Lena Dräger, seminar participants at TU Chemnitz and Johannes Gutenberg Universität Mainz, as well as participants at the 1st annual workshop of the German Science Foundation (DFG) Priority Program 1859 “Experience and Expectations: Historical Foundations of Economic Behaviour” for helpful comments. Michael Braun (GESIS Leibniz Institute for the Social Sciences Mannheim) gave highly valuable comments on an early draft of the survey. We thank the DFG for financial support (project “Macroeconomic Forecasting in Great Crisis” within the Priority Program 1859).

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Appendix A: List of institutions invited

i. Economic research institutes, that are formally politically and economically independent:

  1. 1. German Institute for Economic Research (DIW)

  2. 2. RWI - Leibniz-Institute for Economic Research

  3. 3. Halle Institute of Economic Research (IWH)

  4. 4. Kiel Institute for the World Economy

  5. 5. Ifo Institute – Leibniz Institute for Economic Research at the University of Munich

  6. 6. Institute for Employment Research (IAB)

ii. (Mostly) privately financed forecasting institutions:

  1. 7. Kiel Economics

  2. 8. FERI

  3. 9. Handelsblatt Research Institute

Döpke, Fritsche, Waldhof: Survey of professional forecasters

  1. 10. IHS Global

  2. 11. Hamburg Institute of International Economics (HWWI) [15]

  3. 12. Prognos

iii. Institutes that are financed by interest groups:

  1. 13. Macroeconomic Policy Institute (IMK)

  2. 14. Cologne Institute for Economic Research (IW)

iv. International organizations

  1. 15. International Monetary Fund (IMF)

  2. 16. European Commission (EC)

  3. 17. OECD

v. Political institutions or institutions within the process of economic policy advice

  1. 18. German Council of Economic Experts (Staff)

  2. 19. Federal Ministry for Economic Affairs and Energy

  3. 20. German Bundesbank

vi. Private Firms

  1. 21. Commerzbank

  2. 22. Deutsche Bank Research

  3. 23. Postbank Research

  4. 24. Allianz Economic Research

  5. 25. MM Warburg Research

  6. 26. Helaba Research

  7. 27. Berenberg Bank

  8. 28. DZ Bank

  9. 29. Societe Generale Research

  10. 30. Union Investment

  11. 31. Goldman Sachs

  12. 32. ING Bank Germany

  13. 33. Landesbank Berlin

  14. 34. Sal. Oppenheim

  15. 35. Deka Bank

  16. 36. IKB

  17. 37. NORD LB

  18. 38. Bayern LB

  19. 39. HSBC Trinkaus

  20. 40. LB Baden-Würtenberg

  21. 41. UniCredit

  22. 42. Morgan Stanley

  23. 43. PIMCO

  24. 44. Bremer Landesbank

  25. 45. Degussa

  26. 46. E.on

  27. 47. Collineo

  28. 48. SEB

  29. 49. Berliner Sparkasse

  30. 50. Bank J. Safra Sarasin

vii. Associations:

  1. 51. Bundesverband Deutscher Banken

  2. 52. Chambers of Commerce and Industry (DIHK)

  3. 53. Bundesverband der Deutschen Industrie (BDI)

  4. 54. Mechanical Engineering Industry Association (VDMA)

  5. 55. Bundesverband der deutschen Volks- und Raiffeisenbanken

Appendix B: Answers to free-text questions

Elements of the forecasting process

The following statements have been made in response to the question: ”Which of the following elements do you take into account in your forecasts?”under the category ”other”? (each item corresponds to one respondent)

  1. ”Okonometrische Modelle” (Econometric models)

  2. ”Konjunkturdaten vom aktuellen Rand (Aufträge, Produktion) sowie Entwicklung an den Finanzmärkte und Preisentwicklung für Rohstoffe” (Recent economic data (order inflow, production) as well as the development at financial markets and the prices of raw materials)

  3. ”Erfahrung” (Experience)

  4. ”Faustregeln” (Rules of thumb)

  5. ”Kurzfristige Konjunkturindikatoren” (Short-run business cycle indicators)

  6. ”Okonomische Theorie” (Economic theory)

  7. ”Politökonomische Erwägungen” (Considerations based on political economy)

  8. ”Wissenschaftliche Erkenntnisse” (Scientific insights), ”Institutionelle Kenntnisse” (Institutional knowledge)

  9. ”Historische Erfahrungen” (Historical experiences)

  10. ”Persönliche Einschätzungen” (Personal assessments)

  11. ”Politische Bedürfnisse der höheren Ebenen” (Political necessities of higher levels)

  12. ”Persönliche Prognoseerfahrung” (Personal forecasting experience)

  13. ”Daten, institutionelle Fakten” (Data, institutional facts)

  14. ”Marktentwicklung” (Market developments)

  15. ”Diverse Indikatoren (Industrieproduktion, Einzelhandelsumsätze, Aufträge, Kreditvergabe, … )” (Several indicators (industrial production, retails turnover indices, loans)

  16. ”Prognoseirrtümer der Vergangenheit” (Past forecast errors)

  17. ”Geldpolitik” (Monetary policy)

  18. ”Finanzmarktpreise” (Prices on financial markets)

  19. ”Erfahrungswissen” (Experience-based knowledge)

  20. ”Analysen unterschiedlichster Institute/Okonomen/Analysten” (Analyses of several institutes/economists¨/analysts)

  21. ”Eigene Unternehmensbefragung” (Own survey among firms); – ”Amtliche Statistik” (Official statistics).

Other methods

The following additional or alternative models have been mentioned (each item corresponds to one respondent) in response to the question: ”You have chosen ”Other methods” in the previous question. Please indicate briefly the method(-s) you have in mind and how often they are used.”

  1. ”Zyklusvergleich” (Comparison of cycles) and ”Nicht-parametrische Methoden” (Non-parametric methods)

  2. ”Faustregeln” (Rules of thumb) and ”Historische Elastizitäten” (Historical elasticities)

  3. ”Judgemental adjustments, Horizontal brainstorming”

  4. ”Eigene Umfragen” (Own surveys)

  5. ”Zyklenvergleiche” (Comparison of cycles)

  6. ”Eigene Unternehmensbefragung” (own business survey) (Note: we have skipped additional information to keep the anonymity.)

  7. ”Kurzfristprognose-Modelle (Faktormodelle, Brückengleichungen). Häufig und regelmäßig (alle 2 Wochen).” (Short-term forecasting models, factor models, bridge-equations, often and on a regular basis (every 2 weeks)).

Other theories

The following statements have been made in response to the question: ”You have chosen ”other theories” in the previous question. Please indicate briefly, which theories you have in mind and how important they are.”

  1. ”Debitismus” [16]

  2. ”Klassische Politische Okonomie(,) Marxismus” (Classical political economy, Marxism)

Reasons for forecast errors

The following additional possible reasons of forecast errors have been mentioned (each item corresponds to one respondent):

  1. ”Annahme unveränderter Politik” (Assumption of an unchanged policy)

  2. ”Hohe Komplexität: Die falschen Wirkungszusammenhänge hervorgehoben” (High complexity, the wrong causal relations highlighted)

  3. ”Die Zukunft ist unbekannt.” (The future is unknown)

  4. ”Unvorhergesehen Ereignisse, außer Naturkatastrophen.” (Unforeseen events except natural disasters)

  5. ”Prognosefehler bei exogenen Variablen, die als Input im Modell verwendet werden, z.B. Welthandel, Wechselkurs, Olpreis” (Forecast errors for exogenous variables, that are used as inputs for the model (e. g. world trade, exchange rates, oil prices)

  6. ”Die Frage ist allgemein formuliert, d.h. alle denkbaren Gründe sind irgendwann irgendwo einmal relevant gewesen” (The question is formulated too general, i. e. all possible reasons have been relevant at some place for a certain time.)

  7. ”Die Zukunft ist unbekannt.” (The future is unknown)

  8. ”Ferientage und Saisoneffekte falsch” (Trading days and seasonal effects wrong)

  9. ”Uberbewertung von persönlichen Eindrücken und Stimmungen” (Too much weight for personal im- pressions and sentiments) ”Shit happens”.

  10. ”ökonomische Schocks treten auf, die per Annahme ausgeschlossen wurden.” (Economic shocks occur that have been ruled out by assumption)

Changes due to financial crisis

The following statements have been given in response to the question about what has changed in the forecasting process due to the Financial Crisis:

  1. ”Uberarbeitung bestehender und Schätzung neuer ökonometrischer Modelle (neue Indikatoren, Model Averaging)” (Overhaul of existing and estimation of new econometric models (new indicators, model averaging))

  2. ”Wir sind uns der Ungenauigkeit bewusster, denken in größeren Banbbreiten, legen mehr Wert auf Risikoszenarien” (We are more aware of inaccuracy, think in broader bandwidths, give greater emphasize on risk scenarios)

  3. ”Systematische Prognosefehlerevaluation” (Systematic forecast error evaluation)

  4. ”Literatur zur Prognose ist vielschichtiger geworden und erfordert eingehenderes Studium.” (The literature regarding forecasts has become more complex and demands in-depth studies)

  5. ”Vielfalt der Prognosemethoden und -modelle und Prognosekombination” (Diversity of forecasting methods, models, and combination)

  6. ”Wir schauen starker auf Unsicherheitsmaße, die auf Marktpreisen basieren. Außerdem beachten wir mehr die Bilanzen der Unternehmen und privaten Haushalte, weil laufende Bilanzbereinigungen das Wachstum schwächen. Schließlich sind Blasen wichtiger geworden.” (We are looking more strongly on measures of uncertainty, that rely on market prices. Moreover, consider more strongly the balance sheets of firms and private households, since balance sheet adjustments weaken economic growth. Finally, bubbles have become more important.)

  7. ”Anpassung der eigenen Befragungsmethodik (kürzerer Befraungszeitraum, schnellere Veröffentlichung)” (Adjustment of the own survey technique (shorter survey period, faster publication).)

Demotivation

The following statements have been given in response to the question, what possibly de-motivates forecasters (each item corresponds to one respondent):

  1. ”Konjunkturprognosen sind faktisch irrelevant.” (Business cycle forecasts are - in fact - irrelevant)

  2. ”Dass wenig Zeit für anderes bleibt” (That there is not enough time for other things)

  3. ”Die falsche Wahrnehmung über die Treffsicherheit von Konjunkturprognosen. In der Offentlichkeit¨ und bei Kollegen wird zu wenig anerkannt, wie unsicher (Schocks usw.) das Eintreten von Prognosen ist. Ferner wird dann auf fehlende Kompetenz geschlossen. Das trifft nicht nur auf die Offentlichkeit,¨ sondern auch auf andere Volkswirte anderer Bereiche zu.” (The wrong perception of the forecasts. The public opinion and the colleagues do not sufficiently recognize how uncertain (shocks etc.) the realisation of forecasts is. Moreover, from this it is concluded that forecasters are not competent. This does not only hold for the general public, but also for economist from other areas).

  4. ”Nichts” (Nothing).

  5. ”Politische Einflussnahme” (Political influencing)

  6. ”Das geringe Grundverständnis anderer Wissenschaftler und der Offentlichkeit für die Prognosearbeit (z.B. inhärente Prognosefehler, Aufwand Porognosen zu erstellen, Relevanz für andere Bereiche wie wirtschaftspolitische Bereiche” (The little understanding of other scientist and the public for forecasting work. (e. g., inherent forecast errors, the effort to produce forecasts, the relevance for other areas and areas of economic policy).

  7. ”Nichts davon in relevantem Maße” (Nothing of the above to a relevant extend)

  8. ”Die Datenqualitaet” (Data quality)

  9. ”Die geringe Prognosegüte” (The lack of forecasts accuracy)

  10. ”Ungünstiges Verhältnis von Aufwand (Daten-, Modellupdate, Text schreiben etc.) und Ertrag (Aufmerksamkeit i.S.v. ”in der wirtschaftspolitischen Debatte Gehör finden” (Unfavourable relation of effort (data and model update, writing text, etc.) and rewards (attention in the economic policy debate)

  11. ”Nichts” (Nothing)

  12. ”Keine” (None)

  13. ”Fehlprognosen” (Forecast errors)

  14. ”Nichts” (Nothing)

  15. ”Limited time budget”

  16. ”Der generelle Stress im Beruf” (The general stress in the job) ”Druck bei Fehlprognosen” (Pressure in case of forecast errors).

Other remarks

At the end of the questionnaire, we asked in a free question for general comments, which may have occurred during answering the survey

  1. ”Die Fragen zu Fiskalmultiplikator, Mindestlohn etc empfinde ich als sehr problematisch, da das Situationsbedingte/der Kontext noch viel mehr abgefragt werden müsste” (I see the question regarding the fiscal multiplier, minimum wage etc. as very problematic, since the situational context should have been queried much more precisely)

  2. ”Beim langfristigen Fiskalmultiplikator hätte ich gerne die Möglichkeit gehabt, einen negativen Wert einzugeben.” (As regards the long-run multiplier I would like to had the opportunity to enter a negative value)

  3. ”Mir wären oftmals eindeutige Antwortmöglichkeiten wie ja/nein lieber als diese graduellen Abstufungen.” (I would have preferred clear-cut yes/no-answer opportunities instead of the graduations.)

  4. ”Makroökonomische Konjunkturprognosen sind weit mehr als nur eine möglichst treffsichere Punktprognose für BIP-Wachstum oder Inflation. Jenseits der kurzen Frist (1–2 Quartale) ist die Prognosegüte nicht anhand des Prognosefehlers festzumachen (einfache Vergleichsmodelle wie AR-Prognosen sind dort nämlich kaum zu schlagen), sondern anhand der Konsistenz und Stimmigkeit des Prognosegesamtbildes und seiner verschiedenen Komponenten (”Story” hinter dem Prognose-Basisszeario - dieses stellt die aus Sicht des Prognostikers wahrscheinlichste Entwicklung bedingt auf die exogenen Annahmen und auf die Annahme des Abklingens vergangener ökonomischer Schocks und des Ausbleibens zukünftiger Schocks dar” (Macroeconomic business cycle forecasts are much more than just as precise as possible a point forecast of GDP growth or inflation. Beyond the very short-run time horizon(1–2 quarters) forecast accuracy cannot be measured with a simple forecast error (since simple competing models like AR models are much better in this regard). Rather, forecasts have to be judged by the consistency and coherence of the underlying picture and its different components (the ”story” of the base-scenario of the forecast, which gives the most likely development in the eyes of the forecaster given the assumptions for exogenous factors and the unwinding of past economic shocks and the non-existence of future shocks))

Received: 2018-02-21
Revised: 2018-07-25
Accepted: 2018-10-27
Published Online: 2019-04-12
Published in Print: 2019-04-24

© 2019 Oldenbourg Wissenschaftsverlag GmbH, Published by De Gruyter Oldenbourg, Berlin/Boston

Heruntergeladen am 17.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/jbnst-2018-0018/html
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