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DPB: Dynamic Panel Binary data models in Gretl

Author

Listed:
  • Riccardo Lucchetti

    (Universita' Politecnica delle Marche, Dipartimento di Scienze economiche e sociali)

  • Claudia Pigini

    (Universita' Politecnica delle Marche, Dipartimento di Scienze economiche e sociali)

Abstract

This paper presents the Gretl function package DPB for estimating dynamic binary models with panel data. The package contains routines for the estimation of the random-effects dynamic probit model proposed by Heckman (1981b) and its generalisation by Hyslop (1999) and Keane and Sauer (2009) to accommodate AR(1) disturbances. The fixed-effects estimator by Bartolucci and Nigro (2010) is also implemented. DPB is available on the Gretl function packages archive.

Suggested Citation

  • Riccardo Lucchetti & Claudia Pigini, 2015. "DPB: Dynamic Panel Binary data models in Gretl," gretl working papers 1, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali, revised 24 Apr 2015.
    • Handle: RePEc:anc:wgretl:1
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Elena Giarda & Gloria Moroni, 2018. "The Degree of Poverty Persistence and the Role of Regional Disparities in Italy in Comparison with France, Spain and the UK," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 136(1), pages 163-202, February.
    2. 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".
    3. Miriam Beblo & Sven Schreiber, 2022. "Leisure and housing consumption after retirement: new evidence on the life-cycle hypothesis," Review of Economics of the Household, Springer, vol. 20(1), pages 305-330, March.
    4. Riccardo Lucchetti & Claudia Pigini, 2018. "Dynamic panel probit: finite-sample performance of alternative random-effects estimators," Working Papers 426, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    5. Giulia Bettin & Riccardo Lucchetti, 2016. "Steady streams and sudden bursts: persistence patterns in remittance decisions," Journal of Population Economics, Springer;European Society for Population Economics, vol. 29(1), pages 263-292, January.
    6. Nursel Aydiner-Avsar & M. Burak Onemli, 2023. "Working Poverty in Türkiye: A Dynamic Panel Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 168(1), pages 139-164, August.
    7. Marcin Błażejowski, 2024. "Which C compiler and BLAS/LAPACK library should I use: gretl’s numerical efficiency in different configurations," Computational Statistics, Springer, vol. 39(7), pages 3497-3522, December.
    8. Cucinelli, Doriana & Battista, Maria Luisa Di & Marchese, Malvina & Nieri, Laura, 2018. "Credit risk in European banks: The bright side of the internal ratings based approach," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 213-229.
    9. Bartolucci, Francesco & Pigini, Claudia, 2017. "cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i07).
    10. Riccardo (Jack) Lucchetti & Claudia Pigini, 2020. "Choice of solutions to the initial-conditions problem in dynamic panel probit models," Working Papers 2020:27, Department of Economics, University of Venice "Ca' Foscari".

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    More about this item

    Keywords

    Gretl function package; Random-Effects Dynamic Probit model; Quadratic Exponential model; Gauss-Hermite quadrature; simulated Maximum Likelihood; Conditional Maximum Likelihood;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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