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?