[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-13-00634.html
   My bibliography  Save this article

A multivariate evaluation of German output growth and inflation forecasts

Author

Listed:
  • Jens J. Krüger

    (Darmstadt University of Technology)

Abstract
We examine the joint efficiency of German output growth and inflation forecasts using a multivariate loss function which allows for loss asymmetry and different degrees of curvature. Efficiency is evaluated with respect to financial market variables as stock market returns and interest spreads. Thereby we find evidence that the loss function is approximately linear with a considerable degree of asymmetry. Compared to the situation where the two forecasted variables are considered univariately, forecast efficiency is also rejected more frequently. Adding a forward-looking survey-based expectations indicator leads to even stronger rejections of forecast efficiency.

Suggested Citation

  • Jens J. Krüger, 2014. "A multivariate evaluation of German output growth and inflation forecasts," Economics Bulletin, AccessEcon, vol. 34(3), pages 1410-1418.
  • Handle: RePEc:ebl:ecbull:eb-13-00634
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2014/Volume34/EB-14-V34-I3-P129.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. Ivana Komunjer & Michael T. Owyang, 2012. "Multivariate Forecast Evaluation and Rationality Testing," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1066-1080, November.
    3. Hoffman, Dennis L & Pagan, Adrian R, 1989. "Post-Sample Prediction Tests for Generalized Method of Moments Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 51(3), pages 333-343, August.
    4. 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.
    5. Capistrán, Carlos, 2008. "Bias in Federal Reserve inflation forecasts: Is the Federal Reserve irrational or just cautious?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1415-1427, November.
    6. Krüger, Jens & Hoss, Julian, 2012. "German Business Cycle Forecasts, Asymmetric Loss and Financial Variables," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 63652, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    7. Krüger, Jens J. & Hoss, Julian, 2012. "German business cycle forecasts, asymmetric loss and financial variables," Economics Letters, Elsevier, vol. 114(3), pages 284-287.
    8. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    9. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    10. Patton, Andrew J. & Timmermann, Allan, 2007. "Properties of optimal forecasts under asymmetric loss and nonlinearity," Journal of Econometrics, Elsevier, vol. 140(2), pages 884-918, October.
    11. Krol, Robert, 2013. "Evaluating state revenue forecasting under a flexible loss function," International Journal of Forecasting, Elsevier, vol. 29(2), pages 282-289.
    12. Jörg Döpke & Ulrich Fritsche & Boriss Siliverstovs, 2010. "Evaluating German business cycle forecasts under an asymmetric loss function," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(1), pages 1-18.
    13. Arturo Estrella & Mary R. Trubin, 2006. "The yield curve as a leading indicator: some practical issues," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 12(Jul).
    14. Adrian, Tobias & Estrella, Arturo, 2008. "Monetary tightening cycles and the predictability of economic activity," Economics Letters, Elsevier, vol. 99(2), pages 260-264, May.
    15. Hansen, Lars Peter & Heaton, John & Yaron, Amir, 1996. "Finite-Sample Properties of Some Alternative GMM Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 262-280, July.
    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. 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.

    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. Tsuchiya, Yoichi, 2016. "Asymmetric loss and rationality of Chinese renminbi forecasts: An implication for the trade between China and the US," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 116-127.
    2. Krüger, Jens J. & Hoss, Julian, 2012. "German business cycle forecasts, asymmetric loss and financial variables," Economics Letters, Elsevier, vol. 114(3), pages 284-287.
    3. Tsuchiya, Yoichi, 2016. "Assessing macroeconomic forecasts for Japan under an asymmetric loss function," International Journal of Forecasting, Elsevier, vol. 32(2), pages 233-242.
    4. Tsuchiya, Yoichi, 2012. "Evaluating Japanese corporate executives’ forecasts under an asymmetric loss function," Economics Letters, Elsevier, vol. 116(3), pages 601-603.
    5. Behrens, Christoph & Pierdzioch, Christian & Risse, Marian, 2018. "Testing the optimality of inflation forecasts under flexible loss with random forests," Economic Modelling, Elsevier, vol. 72(C), pages 270-277.
    6. 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.
    7. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2021. "The Rationality of USDA Forecasts under Multivariate Asymmetric Loss," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1006-1033, May.
    8. Giovannelli, Alessandro & Pericoli, Filippo Maria, 2020. "Are GDP forecasts optimal? Evidence on European countries," International Journal of Forecasting, Elsevier, vol. 36(3), pages 963-973.
    9. Pierdzioch, Christian & Rülke, Jan-Christoph & Stadtmann, Georg, 2015. "Central banks’ inflation forecasts under asymmetric loss: Evidence from four Latin-American countries," Economics Letters, Elsevier, vol. 129(C), pages 66-70.
    10. Krol, Robert, 2013. "Evaluating state revenue forecasting under a flexible loss function," International Journal of Forecasting, Elsevier, vol. 29(2), pages 282-289.
    11. Anatolyev, Stanislav, 2009. "Dynamic modeling under linear-exponential loss," Economic Modelling, Elsevier, vol. 26(1), pages 82-89, January.
    12. Capistrán, Carlos, 2008. "Bias in Federal Reserve inflation forecasts: Is the Federal Reserve irrational or just cautious?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1415-1427, November.
    13. Carlos Capistr¡N & Allan Timmermann, 2009. "Disagreement and Biases in Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(2-3), pages 365-396, March.
    14. Issler, João Victor & Soares, Ana Flávia, 2019. "Central Bank credibility and inflation expectations: a microfounded forecasting approach," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 812, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    15. Andrea Betancor & Pablo Pincheira, 2008. "Forecasting Inflation Forecast Errors," Working Papers Central Bank of Chile 477, Central Bank of Chile.
    16. Sinclair, Tara M. & Gamber, Edward N. & Stekler, Herman & Reid, Elizabeth, 2012. "Jointly evaluating the Federal Reserve’s forecasts of GDP growth and inflation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 309-314.
    17. Julieta Caunedo & Riccardo Dicecio & Ivana Komunjer & Michael T. Owyang, 2020. "Asymmetry, Complementarities, and State Dependence in Federal Reserve Forecasts," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(1), pages 205-228, February.
    18. Carlos Capistrán & Allan Timmermann, 2009. "Disagreement and Biases in Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(2‐3), pages 365-396, March.
    19. Wang, Yiyao & Lee, Tae-Hwy, 2014. "Asymmetric loss in the Greenbook and the Survey of Professional Forecasters," International Journal of Forecasting, Elsevier, vol. 30(2), pages 235-245.
    20. Stan Hurn & Jing Tian & Lina Xu, 2021. "Assessing the Informational Content of Official Australian Bureau of Meteorology Forecasts of Wind Speed," The Economic Record, The Economic Society of Australia, vol. 97(319), pages 525-547, December.

    More about this item

    Keywords

    macroeconomic forecasting; asymmetric loss; financial markets; survey expectations;
    All these keywords.

    JEL classification:

    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

    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:ebl:ecbull:eb-13-00634. 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: John P. Conley (email available below). General contact details of provider: .

    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.