What are the differences between the LPM and the logit or probit models?

The choice between LPM, logit, and probit models depends on the specific research question and the characteristics of the data. Each model has its strengths and limitations, and researchers should carefully consider which model best fits their needs.

The LPM, a statistical model commonly used in econometrics, operates under the assumption that the marginal effects on the dependent variable remain constant. This assumption simplifies the analysis by allowing researchers to assume a uniform impact across different levels of the independent variables.

In contrast, the logit and probit models, which are popular choices for modeling binary outcomes, exhibit a different behavior. These models imply that the partial effects, or the impact of independent variables on the dependent variable, diminish in magnitude as the values of the independent variables change.

The diminishing magnitude of partial effects in logit and probit models is an important characteristic that distinguishes them from the LPM. It reflects the idea that as an independent variable approaches its extreme values, the incremental change in the dependent variable becomes smaller.

This difference in assumptions has important implications for the interpretation of model results. In the LPM, researchers can directly interpret the coefficients as the constant marginal effects. However, in logit and probit models, the coefficients represent the change in the log-odds or probability, and the actual marginal effects need to be calculated separately.