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  1. Bayesian mode inference for discrete distributions in economics and finance
    Author: Cross, Jamie; Hoogerheide, Lennart; Labonne, Paul; Dijk, Herman K. van
    Published: 2023
    Publisher:  BI Norwegian Business School, Centre for Applied Macro-Petroleum economics (CAMP), Oslo

    Access:
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    VS 321
    Inter-library loan:
    No inter-library loan
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 11250/3095578
    Series: CAMP working paper series ; no. 2023, 11
    Subjects: Bayesian Inference; Mixture Models; Mode Inference; Multimodality; Shifted-Poisson
    Scope: 1 Online-Ressource (circa 11 Seiten)
  2. Bayes estimates of multimodal density features using DNA and Economic Data
    Author: Basturk, Nalan; Hoogerheide, Lennart; Dijk, Herman K. van
    Published: [2021]
    Publisher:  Tinbergen Institute, Amsterdam, The Netherlands

    In several scientific fields, like bioinformatics, financial and macro-economics, important theoretical and practical issues exist that involve multimodal data distributions. We propose a Bayesian approach using mixtures distributions to approximate... more

    Access:
    Verlag (kostenfrei)
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 432
    Inter-library loan:
    No inter-library loan

     

    In several scientific fields, like bioinformatics, financial and macro-economics, important theoretical and practical issues exist that involve multimodal data distributions. We propose a Bayesian approach using mixtures distributions to approximate accurately such data distributions. Shape and other features of the mixture approximations are estimated including their uncertainty. For discrete data, we introduce a novel mixture of shifted Poisson distributions with an unknown number of components, which overcomes the equidispersion restriction in the standard Poisson which accomodates a wide range of shapes such as multimodality and long tails. Our simulation-based Bayesian inference treats the density features as random variables and highest credibility regions around features are easily obtained. For discrete data we develop an adapted version of the Reversible Jump Markov Chain Monte Carlo (RJMCMC) method, which allows for an unknown number of components instead of the more restrictive approach of choosing a particular number of mixture components using information criteria. Using simulated data, we show that our approach works successfully for three issues that one encounters during the estimation of mixtures: label switching; mixture complexity and prior information and mode membership versus component membership. The proposed method is applied to three empirical data sets: The count data method yields a novel perspective of the data on DNA tandem repeats in Schaap et al. (2013); the bimodal distribution of payment details of clients obtaining a loan from a financial institution in Spain in 1990 gives insight into the repayment ability of individual clients; and the distribution of the modes of real GDP growth data from the PennWorld Tables and their evolution over time explores possible world-wide economic convergence as well as group convergence between the US and European countries. The results of our descriptive analysis may be used as input for forecasting and policy analysis.

     
    Export to reference management software   RIS file
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/237750
    Series: Array ; TI 2021, 017
    Subjects: Multimodality; mixtures; Markov Chain Monte Carlo; Bayesian Inference
    Scope: 1 Online-Ressource (circa 33 Seiten), Illustrationen
  3. Bayesian mode inference for discrete distributions in economics and finance
    Author: Cross, Jamie; Hoogerheide, Lennart; Labonne, Paul; Dijk, Herman K. van
    Published: [2023]
    Publisher:  Tinbergen Institute, Amsterdam, The Netherlands

    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... more

    Access:
    Verlag (kostenfrei)
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 432
    Inter-library loan:
    No inter-library loan

     

    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.

     
    Export to reference management software   RIS file
      BibTeX file
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/273849
    Series: Array ; TI 2023, 038
    Subjects: Bayesian Inference; Mixture Models; Mode Inference; Multimodality; Shifted-Poisson
    Scope: 1 Online-Ressource (circa 11 Seiten), Illustrationen