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A consistent estimator for the binomial distribution in the presence of "incidental parameters": an application to patent data

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  • Machado, Matilde P.

Abstract

In this paper a consistent estimator for the Binomial distribution in the presence of incidental parameters, or fixed effects, when the underlying probability is a logistic function is derived. The consistent estimator is obtained from the maximization of a conditional likelihood function in light of Andersen's work. Monte Carlo simulations show its superiority relative to the traditional maximum likelihood estimator with fixed effects also in small samples, particularly when the number of observations in each cross-section, T, is small. Finally, this new estimator is applied to an original dataset that allows the estimation of the probability of obtaining a patent.
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Suggested Citation

  • Machado, Matilde P., 2004. "A consistent estimator for the binomial distribution in the presence of "incidental parameters": an application to patent data," Journal of Econometrics, Elsevier, vol. 119(1), pages 73-98, March.
  • Handle: RePEc:eee:econom:v:119:y:2004:i:1:p:73-98
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    Cited by:

    1. Rainer Winkelmann & Lin Xu, 2022. "Testing the binomial fixed effects logit model, with an application to female labour supply," Empirical Economics, Springer, vol. 62(2), pages 679-708, February.
    2. David S Jacks & Krishna Pendakur & Hitoshi Shigeoka, 2021. "Infant Mortality and the Repeal of Federal Prohibition," The Economic Journal, Royal Economic Society, vol. 131(639), pages 2955-2983.
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    4. Irene Botosaru & Chris Muris, 2017. "Binarization for panel models with fixed effects," CeMMAP working papers 31/17, Institute for Fiscal Studies.
    5. Mohd Shadab Danish & Pritam Ranjan & Ruchi Sharma, 2022. "Assessing the Impact of Patent Attributes on the Value of Discrete and Complex Innovations," Papers 2208.07222, arXiv.org.
    6. Mohd Shadab Danish & Pritam Ranjan & Ruchi Sharma, 2021. "Identification of “Valuable” Technologies via Patent Statistics in India: An Analysis Based on Renewal Information," BASE University Working Papers 13/2021, BASE University, Bengaluru, India.

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