How are logit and probit models similar?
Could you elaborate on the similarities between logit and probit models? Both are used in statistical analysis to model binary outcomes, but how do they compare in terms of their underlying assumptions, interpretation of coefficients, and the distributions they utilize? I'm particularly interested in understanding the key features that make them similar, and how this impacts their application in finance and cryptocurrency research.