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Using a linear probability model, significant event by support interactions were observed for both depressive symptoms and major depression.
Using a linear probability model instead of a probit model produces similar results.
We will also estimate this specification using a linear probability model in OLS and IV.
10Results for Syria using a linear probability model or FE logit are qualitatively similar and available from the authors upon request.
Including fixed effects in a discrete choice model might generate an incidental parameter problem, which, however, does not occur when using a linear probability model.
Because the dependent variable is a non-continuous dummy variable (equal to 1 if the respondent is employed/in the labor market), I will estimate Eq. (2) using a linear probability model to avoid the concerns raised by Ai and Norton (2003) regarding the reliability of difference-in-differences estimates with probit estimation.
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8 Results are very similar when we use a linear probability model.
Normally, probit specification would be used to analyse this; however, due to inconsistency of probit regression in settings with fixed effects, we use a linear probability model instead17.
While we used a linear probability model for ease of interpretation and use, the error term is heteroskedastic and is not normally distributed, the predicted values are not constrained to be between 0 and 1.
To estimate equation (1) we used a linear probability model, which according to Angrist [ 34] leads to closer results to those obtained in a binary model such as a logit or a probit.
Table 2 presents the importance weights (i.e. parameter estimates) from choice models estimated from the conjoint analysis data using both a linear probability model (estimated via ordinary least squares) and logistic regression.
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