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ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models

Author

Listed:
  • Riccardo (Jack) Lucchetti

    (Dipartimento di Scienze Economiche e Sociali (DiSES), Università Politecnica delle Marche)

  • Luca Pedini

    (Dipartimento di Scienze Economiche e Sociali (DiSES), Università Politecnica delle Marche)

Abstract

This paper describes the gretl function package ParMA, which provides Bayesian model averaging in generalised linear models. In order to over-come the lack of analytical specification for many of the models covered, the package features an implementation of the reversible jump Markov chain Monte Carlo technique, following the original idea by Green (1995), as a flexible tool to model several specifications. Particular attention is devoted to computational aspects such as the automatisation of the model building procedure and the parallelisation of the sampling scheme.

Suggested Citation

  • Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2020:28
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    References listed on IDEAS

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    Cited by:

    1. Riccardo Lucchetti & Luca Pedini & Claudia Pigini, 2021. "Bayesian Model Averaging For Propensity Score Matching In Tax Rebate," Working Papers 457, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

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    More about this item

    Keywords

    BMA; GLM; RJMCMC; parallelisation;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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