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Estimation for dynamic and static panel probit models with large individual effects

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  • Gao, Wei
  • Bergsma, Wicher
  • Yao, Qiwei

Abstract

For discrete panel data, the dynamic relationship between successive observations is often of interest. We consider a dynamic probit model for short panel data. A problem with estimating the dynamic parameter of interest is that the model contains a large number of nuisance parameters, one for each individual. Heckman proposed to use maximum likelihood estimation of the dynamic parameter, which, however, does not perform well if the individual effects are large. We suggest new estimators for the dynamic parameter, based on the assumption that the individual parameters are random and possibly large. Theoretical properties of our estimators are derived, and a simulation study shows they have some advantages compared with Heckman's estimator and the modified profile likelihood estimator for fixed effects.

Suggested Citation

  • Gao, Wei & Bergsma, Wicher & Yao, Qiwei, 2017. "Estimation for dynamic and static panel probit models with large individual effects," LSE Research Online Documents on Economics 65165, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:65165
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    References listed on IDEAS

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    7. Bartolucci, Francesco & Farcomeni, Alessio, 2009. "A Multivariate Extension of the Dynamic Logit Model for Longitudinal Data Based on a Latent Markov Heterogeneity Structure," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 816-831.
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    More about this item

    Keywords

    Dynamic probit regression; generalized linear models; panel data; probit models; static probit regression; EP/L01226X/1;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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