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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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    Cited by:

    1. repec:jss:jstsof:v:078:i07 is not listed on IDEAS
    2. 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.
    3. 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.
    4. 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).

    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;

    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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