IDEAS home Printed from https://ideas.repec.org/p/tin/wpaper/20230038.html
   My bibliography  Save this paper

Bayesian Mode Inference for Discrete Distributions in Economics and Finance

Author

Listed:
  • Jamie Cross

    (University of Melbourne)

  • Lennart Hoogerheide

    (Vrije Universiteit Amsterdam)

  • Paul Labonne

    (Norwegian Business School)

  • Herman K. van Dijk

    (Erasmus University Rotterdam)

Abstract

Detecting heterogeneity within a population is crucial in many economic and financial applications. Econometrically, this requires a credible determination of multimodality in a given data distribution. We propose a straightforward yet effective technique for mode inference in discrete data distributions which involves fitting a mixture of novel shifted-Poisson distributions. The credibility and utility of our proposed approach is demonstrated through empirical investigations on datasets pertaining to loan default risk and inflation expectations.

Suggested Citation

  • Jamie Cross & Lennart Hoogerheide & Paul Labonne & Herman K. van Dijk, 2023. "Bayesian Mode Inference for Discrete Distributions in Economics and Finance," Tinbergen Institute Discussion Papers 23-038/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20230038
    as

    Download full text from publisher

    File URL: https://papers.tinbergen.nl/23038.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Dionne, Georges & Artis, Manuel & Guillen, Montserrat, 1996. "Count data models for a credit scoring system," Journal of Empirical Finance, Elsevier, vol. 3(3), pages 303-325, September.
    2. Pfajfar, Damjan & Santoro, Emiliano, 2010. "Heterogeneity, learning and information stickiness in inflation expectations," Journal of Economic Behavior & Organization, Elsevier, vol. 75(3), pages 426-444, September.
    3. Mary A. Burke & Michael Manz, 2014. "Economic Literacy and Inflation Expectations: Evidence from a Laboratory Experiment," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(7), pages 1421-1456, October.
    4. Haltiwanger, John & Waldman, Michael, 1985. "Rational Expectations and the Limits of Rationality: An Analysis of Heterogeneity," American Economic Review, American Economic Association, vol. 75(3), pages 326-340, June.
    5. Woo, Mi-Ja & Sriram, T.N., 2007. "Robust estimation of mixture complexity for count data," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4379-4392, May.
    6. P. M. Hartigan, 1985. "Computation of the Dip Statistic to Test for Unimodality," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(3), pages 320-325, November.
    Full references (including those not matched with items on IDEAS)

    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. Firmin Doko Tchatoka & Qazi Haque & Madison Terrell, 2022. "Monetary policy shocks and exchange rate dynamics in small open economies," CAMA Working Papers 2022-15, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. Jamie L. Cross & Lennart Hoogerheide & Paul Labonne & Herman K. van Dijk, 2024. "Flexible Negative Binomial Mixtures for Credible Mode Inference in Heterogeneous Count Data from Finance, Economics and Bioinformatics," Tinbergen Institute Discussion Papers 24-056/III, Tinbergen Institute.
    3. Hommes, Cars, 2011. "The heterogeneous expectations hypothesis: Some evidence from the lab," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 1-24, January.
    4. Cars Hommes, 2010. "The heterogeneous expectations hypothesis: some evidence from the lab," Post-Print hal-00753041, HAL.
    5. Pfajfar, Damjan, 2013. "Formation of rationally heterogeneous expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1434-1452.
    6. Nalan Basturk & Lennart Hoogerheide & Herman K. van Dijk, 2021. "Bayes estimates of multimodal density features using DNA and Economic Data," Tinbergen Institute Discussion Papers 21-017/III, Tinbergen Institute.
    7. Umashanger, T. & Sriram, T.N., 2009. "L2E estimation of mixture complexity for count data," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4243-4254, October.
    8. Haltiwanger, John & Waldman, Michael, 1991. "Responders versus Non-responders: A New Perspective on Heterogeneity," Economic Journal, Royal Economic Society, vol. 101(408), pages 1085-1102, September.
    9. Paul Hubert, 2014. "FOMC Forecasts as a Focal Point for Private Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(7), pages 1381-1420, October.
    10. Mariani, Fabio & Pérez-Barahona, Agustín & Raffin, Natacha, 2010. "Life expectancy and the environment," Journal of Economic Dynamics and Control, Elsevier, vol. 34(4), pages 798-815, April.
    11. Olga Shurchkov, 2013. "Coordination and learning in dynamic global games: experimental evidence," Experimental Economics, Springer;Economic Science Association, vol. 16(3), pages 313-334, September.
    12. Natalia Mishagina & Claude Montmarquette, 2018. "The Demand for Economic Policies, Beliefs, and Willingness-to-Pay: The Case of the Minimum Wage Policy in Quebec," CIRANO Project Reports 2018rp-14, CIRANO.
    13. Campbell, Carl M., 2014. "The formation of wage expectations in the effort and quit decisions of workers," Economic Modelling, Elsevier, vol. 42(C), pages 313-322.
    14. Eizo Akiyama & Nobuyuki Hanaki & Ryuichiro Ishikawa, 2017. "It is Not Just Confusion! Strategic Uncertainty in An Experimental Asset Market," Economic Journal, Royal Economic Society, vol. 127(605), pages 563-580, October.
    15. Cornand, Camille & Hubert, Paul, 2020. "On the external validity of experimental inflation forecasts: A comparison with five categories of field expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    16. Ilek, Alex, 2021. "Are monetary surprises effective? The view of professional forecasters in Israel," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 516-530.
    17. Ernst Fehr & Jean-Robert Tyran, 1999. "Does Money Illusion Matter? An Experimental Approach," CESifo Working Paper Series 184, CESifo.
    18. Tiziana Assenza & William A. Brock & Cars H. Hommes, 2013. "Animal Spirits, Heterogeneous Expectations and the Emergence of Booms and Busts," Tinbergen Institute Discussion Papers 13-205/II, Tinbergen Institute.
    19. Deversi, Marvin & Ispano, Alessandro & Schwardmann, Peter, 2021. "Spin doctors: An experiment on vague disclosure," European Economic Review, Elsevier, vol. 139(C).
    20. Colasante, Annarita & Palestrini, Antonio & Russo, Alberto & Gallegati, Mauro, 2017. "Adaptive expectations versus rational expectations: Evidence from the lab," International Journal of Forecasting, Elsevier, vol. 33(4), pages 988-1006.

    More about this item

    Keywords

    Bayesian Inference; Mixture Models; Mode Inference; Multimodality; Shifted-Poisson.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E00 - Macroeconomics and Monetary Economics - - General - - - General
    • D00 - Microeconomics - - General - - - General

    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:tin:wpaper:20230038. 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: Tinbergen Office +31 (0)10-4088900 (email available below). General contact details of provider: https://edirc.repec.org/data/tinbenl.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.