Accounting for persistence in panel count data models. An application to the number of patents awarded
Economics Letters, 2018, vol. 171, issue C, 245-248
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.
Keywords: Dynamics; Initial conditions; Latent heterogeneity; Markov Chain Monte Carlo; Panel count data; Serial correlation (search for similar items in EconPapers)
JEL-codes: C1 C5 C11 C13 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:171:y:2018:i:c:p:245-248
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