IDEAS home Printed from https://ideas.repec.org/p/ecb/ecbwps/20212512.html
   My bibliography  Save this paper

Statistical decision functions with judgment

Author

Listed:
  • Manganelli, Simone

Abstract

A decision maker tests whether the gradient of the loss function evaluated at a judgmental decision is zero. If the test does not reject, the action is the judgmental decision. If the test rejects, the action sets the gradient equal to the boundary of the rejection region. This statistical decision rule is admissible and conditions on the sample realization. The confidence level reflects the decision maker’s aversion to statistical uncertainty. The decision rule is applied to a problem of asset allocation. JEL Classification: C1, C11, C12, C13, D81

Suggested Citation

  • Manganelli, Simone, 2021. "Statistical decision functions with judgment," Working Paper Series 2512, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20212512
    Note: 196912
    as

    Download full text from publisher

    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2512~b03b48b458.en.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Geweke, John & Whiteman, Charles, 2006. "Bayesian Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 1, pages 3-80, Elsevier.
    2. Manganelli, Simone, 2023. "Double conditioning: the hidden connection between Bayesian and classical statistics," Working Paper Series 2786, European Central Bank.
    3. Larry G. Epstein & Martin Schneider, 2010. "Ambiguity and Asset Markets," Annual Review of Financial Economics, Annual Reviews, vol. 2(1), pages 315-346, December.
    4. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    5. Chamberlain, Gary, 2000. "Econometrics and decision theory," Journal of Econometrics, Elsevier, vol. 95(2), pages 255-283, April.
    6. Andrew Patton & Allan Timmermann, 2012. "Forecast Rationality Tests Based on Multi-Horizon Bounds," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 1-17.
    7. Manski, Charles F., 2013. "Public Policy in an Uncertain World: Analysis and Decisions," Economics Books, Harvard University Press, number 9780674066892, Spring.
    8. Charles F. Manski, 2021. "Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald," Econometrica, Econometric Society, vol. 89(6), pages 2827-2853, November.
    9. Graham Elliott & Allan Timmermann, 2016. "Economic Forecasting," Economics Books, Princeton University Press, edition 1, number 10740.
    10. Charles F. Manski, 2013. "Response to the Review of ‘Public Policy in an Uncertain World’," Economic Journal, Royal Economic Society, vol. 0, pages 412-415, August.
    11. Gelain, Paolo & Manganelli, Simone, 2020. "Monetary policy with judgment," Working Paper Series 2404, European Central Bank.
    12. Graham Elliott & Allan Timmermann, 2016. "Forecasting in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 81-110, October.
    13. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    14. Manganelli, Simone, 2009. "Forecasting With Judgment," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 553-563.
    15. Daniel Ellsberg, 1961. "Risk, Ambiguity, and the Savage Axioms," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 75(4), pages 643-669.
    16. Granger, Clive W.J. & Machina, Mark J., 2006. "Forecasting and Decision Theory," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 2, pages 81-98, Elsevier.
    17. Isaiah Andrews & Anna Mikusheva, 2022. "Optimal Decision Rules for Weak GMM," Econometrica, Econometric Society, vol. 90(2), pages 715-748, March.
    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. Manganelli, Simone, 2023. "Double conditioning: the hidden connection between Bayesian and classical statistics," Working Paper Series 2786, European Central Bank.

    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. Manganelli, Simone, 2016. "Deciding with judgment," Working Paper Series 1947, European Central Bank.
    2. João F. Caldeira & Guilherme V. Moura & Francisco J. Nogales & André A. P. Santos, 2017. "Combining Multivariate Volatility Forecasts: An Economic-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 15(2), pages 247-285.
    3. Cosmin L. Ilut & Martin Schneider, 2022. "Modeling Uncertainty as Ambiguity: a Review," NBER Working Papers 29915, National Bureau of Economic Research, Inc.
    4. Gloria González‐Rivera & C. Vladimir Rodríguez‐Caballero & Esther Ruiz, 2024. "Expecting the unexpected: Stressed scenarios for economic growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 926-942, August.
    5. Dennis Kant & Andreas Pick & Jasper de Winter, 2025. "Nowcasting GDP using machine learning methods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 109(1), pages 1-24, March.
    6. Eric Danan & Thibault Gajdos & Brian Hill & Jean-Marc Tallon, 2016. "Robust Social Decisions," American Economic Review, American Economic Association, vol. 106(9), pages 2407-2425, September.
    7. Garces, Len Patrick Dominic M. & Shen, Yang, 2025. "Robust optimal investment and consumption strategies with portfolio constraints and stochastic environment," European Journal of Operational Research, Elsevier, vol. 322(2), pages 693-712.
    8. Diebold, Francis X. & Shin, Minchul & Zhang, Boyuan, 2023. "On the aggregation of probability assessments: Regularized mixtures of predictive densities for Eurozone inflation and real interest rates," Journal of Econometrics, Elsevier, vol. 237(2).
    9. Jiang, Julia & Liu, Jun & Tian, Weidong & Zeng, Xudong, 2022. "Portfolio concentration, portfolio inertia, and ambiguous correlation," Journal of Economic Theory, Elsevier, vol. 203(C).
    10. Hill, Brian, 2023. "Beyond uncertainty aversion," Games and Economic Behavior, Elsevier, vol. 141(C), pages 196-222.
    11. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023. "Machine learning advances for time series forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
    12. Ari Hyytinen & Petri Rouvinen & Mika Pajarinen & Joosua Virtanen, 2023. "Ex Ante Predictability of Rapid Growth: A Design Science Approach," Entrepreneurship Theory and Practice, , vol. 47(6), pages 2465-2493, November.
    13. Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023. "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.
    14. Guiso, Luigi & Sodini, Paolo, 2013. "Household Finance: An Emerging Field," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1397-1532, Elsevier.
    15. Arai, Natsuki, 2020. "Investigating the inefficiency of the CBO’s budgetary projections," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1290-1300.
    16. Hari P. Regmi & Todd H. Kuethe, 2024. "An evaluation of Congressional Budget Office's baseline projections of USDA mandatory farm and nutrition programs," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 46(3), pages 1214-1240, September.
    17. Molinari, Francesca, 2020. "Microeconometrics with partial identification," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 355-486, Elsevier.
    18. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    19. Yin, Anwen, 2020. "Equity premium prediction and optimal portfolio decision with Bagging," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    20. Philipp Eisenhauer & Lena Janys & Christopher Walsh & Janós Gabler, 2023. "Structural Models for Policy-Making," CRC TR 224 Discussion Paper Series crctr224_2023_484, University of Bonn and University of Mannheim, Germany.

    More about this item

    Keywords

    conditional inference.; confidence intervals; hypothesis testing; statistical decision theory;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

    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:ecb:ecbwps:20212512. 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: Official Publications (email available below). General contact details of provider: https://edirc.repec.org/data/emieude.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.