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I am currently working with a dataframe containing financial information post IPO of traditional IPO firms as well as firms that went public through a SPAC merger, both in the years 2020 to 2022. I am trying to model the likelihood of a firm becoming public using the SPAC merger route, from a few key financial post IPO variables (independent variables). I want to employ a logistic regression model with the dependent variable P(SPAC)i, which is binary and equals 1 for SPAC firms and 0 for IPO firms. The main specification is:

P(SPAC)i = 1⁄(1+ e∧(α + β1Xi + β2Xi + β3Xi + ... + ∑βj Year fixed effectsi,j + ∑βl Industry fixed effects i,l + u i))

Where individual firms are indexed by i.

I don't know how to include year and industry fixed effect into my logit regression. Could anybody give me a hand on this?

Marie-Pier St-Vincent
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  • "I don't know how to include year and industry fixed effect into my logit regression." - for Stack Overflow, please rework this question to make it a specific programming problem. Or consider posting the question on stats.stackexchange.com instead if you need help with the statistical methodology. – AlexK Oct 05 '22 at 07:12
  • I would suggest reading these posts: https://stats.stackexchange.com/questions/37706/logistic-regression-fixed-effects-for-firms-countries-years, https://stats.stackexchange.com/questions/581390/implementing-a-conditional-logit-in-python-statsmodels, and perhaps other texts you can find, which should give you an idea of the complexity of including fixed effects in a non-linear model and why it's not possible to give you an exact solution without a better understanding of your research question, your data, assumptions you are willing to make, etc. – AlexK Oct 05 '22 at 07:12
  • you can include year and firm dummy variables. The easiest way is to use pandas categorical or the formula interface for categorical variables in statsmodels/patsy. – Josef Oct 05 '22 at 14:56

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