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I am interested in examining inventor features on their inventive performance using patent data. I have an unbalanced panel data of 7000 inventor-year observations on 3000 inventors over 15 years within one firm. The results of the inventor- and year-fixed effect model are very different from these of the random effect with the year-fixed effect only. The result of Hauseman test is significant, suggesting that I should use the fixed effect model rather than random effect to control inventor unobservable heterogeneity.

I guess the difference comes from the huge number of control variables – more than 3000 inventor dummies. As I have 3000 inventors and by average each inventor has only 3.5 observations, I am wondering which method I should choose and what I should do.

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  • $\begingroup$ A huge number of categorical variables itself suggests the possibility of forming another level in the multilevel hierarchy. $\endgroup$ – Alexis Feb 22 '19 at 21:17
  • $\begingroup$ Thanks, Alexis. Do you mean I need to classify inventors into different groups and only control the group fixed effect? $\endgroup$ – Ocean Blue Feb 23 '19 at 5:52
  • $\begingroup$ "classify inventors into different groups" was what I meant, I am agnostic about "only control the group fixed effect?" because I do not know the data, the design or the substantive question of interest. $\endgroup$ – Alexis Feb 23 '19 at 17:31
  • $\begingroup$ Thank you very much, Alexis. My question is to examine the effect of structural features of inventors in their collaboration network on their inventive performance using patent data. I have an unbalanced panel data of 7000 inventor-year observations on 3000 inventors over 15 years within one firm. The result of Hauseman test is significant, suggesting that I should use the fixed effect model. $\endgroup$ – Ocean Blue Feb 24 '19 at 18:13

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