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Accounting for persistence in panel count data models. An application to the number of patents awarded

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  • Dimitrakopoulos, Stefanos

Abstract

We propose a Poisson regression model that controls for three potential sources of persistence in panel count data; dynamics, latent heterogeneity and serial correlation in the idiosyncratic errors. We also account for the initial conditions problem. For model estimation, we develop a Markov Chain Monte Carlo algorithm. The proposed methodology is illustrated by a real example on the number of patents granted.

Suggested Citation

  • Dimitrakopoulos, Stefanos, 2018. "Accounting for persistence in panel count data models. An application to the number of patents awarded," Economics Letters, Elsevier, vol. 171(C), pages 245-248.
    • Handle: RePEc:eee:ecolet:v:171:y:2018:i:c:p:245-248
    DOI: 10.1016/j.econlet.2018.08.004
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    References listed on IDEAS

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    More about this item

    Keywords

    Dynamics; Initial conditions; Latent heterogeneity; Markov Chain Monte Carlo; Panel count data; Serial correlation;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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