IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v18y2002i1p85-105.html
   My bibliography  Save this article

Increasing the transparency of macroeconometric forecasts: a report from the trenches

Author

Listed:
  • Heilemann, Ullrich

Abstract

No abstract is available for this item.

Suggested Citation

  • Heilemann, Ullrich, 2002. "Increasing the transparency of macroeconometric forecasts: a report from the trenches," International Journal of Forecasting, Elsevier, vol. 18(1), pages 85-105.
    • Handle: RePEc:eee:intfor:v:18:y:2002:i:1:p:85-105
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169-2070(01)00113-3
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Heilemann, Ullrich & Wolters, Jürgen (ed.), 1998. "Gesamtwirtschaftliche Modelle in der Bundesrepublik Deutschland: Erfahrungen und Perspektiven," RWI Schriften, RWI - Leibniz-Institut für Wirtschaftsforschung, volume 61, number 61.
    2. Fintzen, David & Stekler, H. O., 1999. "Why did forecasters fail to predict the 1990 recession?," International Journal of Forecasting, Elsevier, vol. 15(3), pages 309-323, July.
    3. Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(4), pages 671-690.
    4. Michael Sapir, 1949. "Review of Economic Forecasts for the Transition Period," NBER Chapters, in: Studies in Income and Wealth, Volume 11, pages 273-368, National Bureau of Economic Research, Inc.
    5. Pagan, Adrian, 1989. "On the role of simulation in the statistical evaluation of econometric models," Journal of Econometrics, Elsevier, vol. 40(1), pages 125-139, January.
    6. Michael K. Evans & Yoel Haitovsky & George I. Treyz & Vincent Su, 1972. "An Analysis of the Forecasting Properties of U.S. Econometric Models," NBER Chapters, in: Econometric Models of Cyclical Behavior, Volumes 1 and 2, pages 949-1158, National Bureau of Economic Research, Inc.
    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. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
    2. Herman O. Stekler, 2008. "What Do We Know About G-7 Macro Forecasts?," Working Papers 2008-009, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    3. Ullrich Heilemann & Herman Stekler, 2010. "Perspectives on Evaluating Macroeconomic Forecasts," Working Papers 2010-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    4. Klinger, Sabine & Heilemann, Ullrich, 2005. "Zu wenig Wettbewerb? Zu Stand und Entwicklung der Genauigkeit makroökonomischer Prognosen," Technical Reports 2005,16, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    5. Herman O. Stekler & Raj M. Talwar, 2011. "Economic Forecasting in the Great Recession," Working Papers 2011-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    6. Heilemann, Ullrich & Stekler, H. O., 2003. "Has the accuracy of German macroeconomic forecasts improved?," Technical Reports 2003,31, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    7. Frantisek Brazdik & Zuzana Humplova & Frantisek Kopriva, 2014. "Evaluating a Structural Model Forecast: Decomposition Approach," Research and Policy Notes 2014/02, Czech National Bank.
    8. Engelke, Carola & Heinisch, Katja & Schult, Christoph, 2019. "How forecast accuracy depends on conditioning assumptions," IWH Discussion Papers 18/2019, Halle Institute for Economic Research (IWH).

    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. Heilemann, Ullrich, 1999. "Forecasting with macroeconometric models: A report from the trenches," Technical Reports 1999,47, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. 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.
    3. Albert Ando & Flint Brayton, 1993. "Prices, Wages, and Employment in the U.S. Economy: A Traditional Model and Tests of Some Alternatives," NBER Working Papers 4568, National Bureau of Economic Research, Inc.
    4. Ericsson, Neil R., 1992. "Parameter constancy, mean square forecast errors, and measuring forecast performance: An exposition, extensions, and illustration," Journal of Policy Modeling, Elsevier, vol. 14(4), pages 465-495, August.
    5. Neil R. Ericsson & Jaime R. Marquez, 1990. "Evaluating the predictive performance of trade-account models," International Finance Discussion Papers 377, Board of Governors of the Federal Reserve System (U.S.).
    6. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    7. Bentes, Sonia R. & Menezes, Rui, 2013. "On the predictability of realized volatility using feasible GLS," Journal of Asian Economics, Elsevier, vol. 28(C), pages 58-66.
    8. Clemente De Lucia, 2007. "Did the FED Inflate a Housing Price Bubble? A Cointegration Analysis between the 1980s and the 1990s," ISAE Working Papers 82, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    9. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    10. Michael P. Clements, 2014. "US Inflation Expectations and Heterogeneous Loss Functions, 1968–2010," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 1-14, January.
    11. Bespalova, Olga, 2018. "Forecast Evaluation in Macroeconomics and International Finance. Ph.D. thesis, George Washington University, Washington, DC, USA," MPRA Paper 117706, University Library of Munich, Germany.
    12. F. Gonzalez Miranda & N. Burgess, 1997. "Modelling market volatilities: the neural network perspective," The European Journal of Finance, Taylor & Francis Journals, vol. 3(2), pages 137-157.
    13. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    14. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740.
    15. Mehra, Yash P., 2002. "Level and growth policy rules and actual Fed policy since 1979," Journal of Economics and Business, Elsevier, vol. 54(6), pages 575-594.
    16. Dimitrios Papastamos & Fotis Mouzakis & Simon Stevenson, 2014. "Rationality and Momentum in Real Estate Investment Forecasts," Real Estate & Planning Working Papers rep-wp2014-07, Henley Business School, University of Reading.
    17. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    18. Pablo Pincheira-Brown & Andrea Bentancor & Nicolás Hardy, 2023. "An Inconvenient Truth about Forecast Combinations," Mathematics, MDPI, vol. 11(18), pages 1-24, September.
    19. Elkin Castaño V. & Luis Fernando Melo Velandia, 1998. "Métodos De Combinación De Pronósticos:Una Aplicación A La Inflación Colombiana," Borradores de Economia 3212, Banco de la Republica.
    20. Lars-Erik Öller & Lasse Koskinen, 2004. "A classifying procedure for signalling turning points," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 197-214.

    More about this item

    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:eee:intfor:v:18:y:2002:i:1:p:85-105. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.