IDEAS home Printed from https://ideas.repec.org/p/imk/studie/54-2017.html
   My bibliography  Save this paper

Vergleichende Evaluation der Konjunkturprognosen des Instituts für Makroökonomie und Konjunkturforschung an der Hans-Böckler-Stiftung für den Zeitraum 2005-2014

Author

Listed:
  • Ulrich Fritsche
  • Artur Tarassow

Abstract

Es werden die makroökonomischen Prognosen der acht großen deutschen Forschungsinstitute evaluiert. Die Datenbasis umfasst die publizierten Prognosen für 11 Aggregate für den Zeitraum 2005 bis 2014. Zunächst wird jede Variable separat mittels aktueller Evaluationsmethoden untersucht. Diese umfassen: Theil'sches U, Diebold-Mariano-Test, regressionsbasierte sowie nicht-parametrische Tests auf Rationalität, Bestimmung einer asymmetrischen Prognoseverlustfunktion und den dazugehörigen verallgemeinerten Test auf Rationalität, Richtungsänderungstest. Anschließend wird ein Gesamtsieger mittels des multivariaten Mahalanobis-Distanzmaßes ermittelt. Dieser Ansatz untersucht mehrere Variablen gemeinsam auf ihre empirische Kohärenz. Die quantitative Prognosegüte ist sowohl über die Institute wie über die Variablen hinweg heterogen. Für die meisten berücksichtigten Aggregate - insbesondere für Wirtschaftswachstum (BIP) und Inflationsrate - und über die meisten Institute hinweg betrachtet ist auch eine Prognoseverbesserung gegenüber einer naiven Prognose darstellbar. Allerdings ist ein gewisses Problem mit der Prognosegüte von privaten Konsumausgaben sowie der Arbeitslosenquote sichtbar. Es gibt kaum Hinweise auf Verletzung des Rationalitätskriteriums für das BIP, die Inflationsrate, die Ausrüstungsinvestitionen, die Ein- und Ausfuhren sowie die Arbeitslosenquote. Schwierigkeiten bei den Wendepunktprognosen gibt es für die Inflationsrate, die Konsumausgaben des Staates und der privaten Haushalte, die Bauinvestitionen sowie die Arbeitslosenquote. Im Hinblick auf die Form der Verlustfunktion kann für die Wachstumsrate des BIP oder die Inflationsrate keine Asymmetrie diagnostiziert werden. Bei der multivariaten Prognosegüte ergeben sich überraschend deutliche Unterschiede bezüglich der kohärenten "Stories" zwischen den Instituten. Das IMK schneidet hier mit dem ifo Institut sowie dem IWH zusammen am besten ab. Alle Daten und Computerroutinen sind öffentlich verfügbar.

Suggested Citation

  • Ulrich Fritsche & Artur Tarassow, 2017. "Vergleichende Evaluation der Konjunkturprognosen des Instituts für Makroökonomie und Konjunkturforschung an der Hans-Böckler-Stiftung für den Zeitraum 2005-2014," IMK Studies 54-2017, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    • Handle: RePEc:imk:studie:54-2017
    as

    Download full text from publisher

    File URL: http://www.boeckler.de/pdf/p_imk_study_54_2017.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
    3. Graham Elliott & Allan Timmermann & Ivana Komunjer, 2005. "Estimation and Testing of Forecast Rationality under Flexible Loss," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(4), pages 1107-1125.
    4. Campbell, Bryan & Ghysels, Eric, 1995. "Federal Budget Projections: A Nonparametric Assessment of Bias and Efficiency," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 17-31, February.
    5. Jean‐Marie Dufour, 1981. "Rank Tests For Serial Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 2(3), pages 117-128, May.
    6. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    7. Joseph E. Stiglitz, 2011. "Rethinking Macroeconomics: What Failed, And How To Repair It," Journal of the European Economic Association, European Economic Association, vol. 9(4), pages 591-645, August.
    8. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters, in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46, National Bureau of Economic Research, Inc.
    9. Fildes, Robert & Stekler, Herman, 2002. "Reply to the comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 503-505, December.
    10. Ulrich Fritsche & Jörg Döpke, 2006. "Treffgenauigkeit, Rationalität und Streuung von Konjunkturprognosen für Deutschland," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 75(2), pages 34-53.
    11. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    12. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    13. Kirchgaässner Gebhard & Savioz Marcel, 2001. "Monetary Policy and Forecasts for Real GDP Growth: An Empirical Investigation for the Federal Republic of Germany," German Economic Review, De Gruyter, vol. 2(4), pages 339-365, December.
    14. Tietzel Manfred, 1989. "Prognoselogik oder: Warum Prognostiker irren dürfen/On the Logic of Economic Forecasting," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 206(6), pages 544-560, May.
    15. Gebhardt Kirschgässner & Marcel Savioz, 2001. "Monetary Policy and Forecasts for Real GDP Growth: An Empirical Investigation for the Federal Republic of Germany," German Economic Review, Verein für Socialpolitik, vol. 2(4), pages 339-365, November.
    16. Döhrn Roland & Schmidt Christoph M., 2011. "Information or Institution?: On the Determinants of Forecast Accuracy," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 9-27, February.
    17. Hans Christian Müller-Dröge & Tara M. Sinclair & H.O. Stekler, 2014. "Evaluating Forecasts of a Vector of Variables: a German Forecasting Competition," CAMA Working Papers 2014-55, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    18. Bryan Campbell & Eric Ghysels, 1997. "An Empirical Analysis of the Canadian Budget Process," Canadian Journal of Economics, Canadian Economics Association, vol. 30(3), pages 553-576, August.
    19. Mason Gaffney, 2011. "An Award for Calling the Crash," Econ Journal Watch, Econ Journal Watch, vol. 8(2), pages 185-192, May.
    20. Stekler, H O, 1972. "An Analysis of Turning Point Forecasts," American Economic Review, American Economic Association, vol. 62(4), pages 724-729, September.
    21. Jacob A. Mincer, 1969. "Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance," NBER Books, National Bureau of Economic Research, Inc, number minc69-1, December.
    22. Patton, Andrew J. & Timmermann, Allan, 2007. "Testing Forecast Optimality Under Unknown Loss," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1172-1184, December.
    23. Batchelor, Roy & Peel, David A., 1998. "Rationality testing under asymmetric loss," Economics Letters, Elsevier, vol. 61(1), pages 49-54, October.
    24. Dufour, J.M., 1979. "Rank Tests for Serial Dependence," Cahiers de recherche 7815, Universite de Montreal, Departement de sciences economiques.
    25. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    26. Nigel Pain & Christine Lewis & Thai-Thanh Dang & Yosuke Jin & Pete Richardson, 2014. "OECD Forecasts During and After the Financial Crisis: A Post Mortem," OECD Economics Department Working Papers 1107, OECD Publishing.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Müller, Karsten, 2020. "German forecasters' narratives: How informative are German business cycle forecast reports?," Working Papers 23, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    2. Döpke, Jörg & Fritsche, Ulrich & Waldhof, Gaby, 2017. "Theories, techniques and the formation of German business cycle forecasts. Evidence from a survey among professional forecasters," Working Papers 2, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    3. Döpke Jörg & Fritsche Ulrich & Waldhof Gabi, 2019. "Theories, Techniques and the Formation of German Business Cycle Forecasts : Evidence from a survey of professional forecasters," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(2), pages 203-241, April.
    4. Jörg Döpke & Ulrich Fritsche & Karsten Müller, 2018. "Has Macroeconomic Forecasting changed after the Great Recession? - Panel-based Evidence on Accuracy and Forecaster Behaviour from Germany," Macroeconomics and Finance Series 201803, University of Hamburg, Department of Socioeconomics.
    5. Döpke Jörg & Fritsche Ulrich & Waldhof Gabi, 2019. "Theories, Techniques and the Formation of German Business Cycle Forecasts : Evidence from a survey of professional forecasters," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(2), pages 203-241, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. G. Kontogeorgos & K. Lambrias, 2022. "Evaluating the Eurosystem/ECB staff macroeconomic projections: The first 20 years," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 213-229, March.
    2. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    3. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    4. Lena Dr䧥r & Jan-Oliver Menz & Ulrich Fritsche, 2014. "Perceived inflation under loss aversion," Applied Economics, Taylor & Francis Journals, vol. 46(3), pages 282-293, January.
    5. Jörg Döpke & Ulrich Fritsche & Karsten Müller, 2018. "Has Macroeconomic Forecasting changed after the Great Recession? - Panel-based Evidence on Accuracy and Forecaster Behaviour from Germany," Macroeconomics and Finance Series 201803, University of Hamburg, Department of Socioeconomics.
    6. Karsten Müller, 2022. "German forecasters’ narratives: How informative are German business cycle forecast reports?," Empirical Economics, Springer, vol. 62(5), pages 2373-2415, May.
    7. Dovern, Jonas & Jannsen, Nils, 2017. "Systematic errors in growth expectations over the business cycle," International Journal of Forecasting, Elsevier, vol. 33(4), pages 760-769.
    8. Ericsson, Neil R., 2016. "Eliciting GDP forecasts from the FOMC’s minutes around the financial crisis," International Journal of Forecasting, Elsevier, vol. 32(2), pages 571-583.
    9. Patrick Schmidt & Matthias Katzfuss & Tilmann Gneiting, 2021. "Interpretation of point forecasts with unknown directive," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 728-743, September.
    10. Ericsson, Neil R., 2017. "How biased are U.S. government forecasts of the federal debt?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 543-559.
    11. Peter, Eckley, 2015. "(Non)rationality of consumer inflation perceptions," MPRA Paper 77082, University Library of Munich, Germany.
    12. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    13. Croushore Dean, 2010. "An Evaluation of Inflation Forecasts from Surveys Using Real-Time Data," The B.E. Journal of Macroeconomics, De Gruyter, vol. 10(1), pages 1-32, May.
    14. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    15. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    16. Michael P. Clements, 2022. "Forecaster Efficiency, Accuracy, and Disagreement: Evidence Using Individual‐Level Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(2-3), pages 537-568, March.
    17. repec:onb:oenbwp:y::i:91:b:1 is not listed on IDEAS
    18. Ullrich Heilemann & Karsten Müller, 2018. "Wenig Unterschiede – Zur Treffsicherheit Internationaler Prognosen und Prognostiker [Few differences—on the accuracy of international forecasts and forecaster]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 12(3), pages 195-233, December.
    19. Sergey V. Smirnov & Daria A. Avdeeva, 2016. "Wishful Bias in Predicting Us Recessions: Indirect Evidence," HSE Working papers WP BRP 135/EC/2016, National Research University Higher School of Economics.
    20. Moser, Gabriel & Rumler, Fabio & Scharler, Johann, 2007. "Forecasting Austrian inflation," Economic Modelling, Elsevier, vol. 24(3), pages 470-480, May.
    21. repec:lan:wpaper:413 is not listed on IDEAS
    22. Bürgi, Constantin, 2017. "Bias, rationality and asymmetric loss functions," Economics Letters, Elsevier, vol. 154(C), pages 113-116.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:imk:studie:54-2017. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sabine Nemitz (email available below). General contact details of provider: https://edirc.repec.org/data/imkhbde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.