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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

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    File URL: https://papers.tinbergen.nl/23038.pdf
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    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)

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    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

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