Journal Article


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

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.

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Authors

Dimitrakopoulos, Stefanos

Oxford Brookes departments

Oxford Brookes Business School\Oxford Brookes Business School\Department of Accounting, Finance and Economics

Dates

Year of publication: 2018
Date of RADAR deposit: 2018-08-17

Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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This RADAR resource is the Accepted Manuscript of Accounting for persistence in panel count data models: An application to the number of patents awarded

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