How to interpret the probit model?

The observed outcome, y, is derived from the latent outcome y* in a straightforward manner. If y* is greater than zero, y takes the value of 1; otherwise, y is 0. This thresholding process transforms the continuous latent outcome into a binary observed outcome.

The probit model serves as a valuable tool for analyzing data where the outcome of interest is binary in nature. At its core, it models an unobserved or latent outcome, denoted as y*, which is a linear combination of explanatory variables and an error term.

The coefficients in the probit model represent the marginal effects of the explanatory variables on the latent outcome y*. These coefficients indicate the direction and magnitude of the relationship between each explanatory variable and the latent outcome, holding all other variables constant.

In this context, the latent outcome y* is expressed as a function of the intercept (b0) and a series of coefficients (b1, b2,..., bn) multiplied by their respective explanatory variables (x1, x2,..., xn). This structure allows for the quantification of the relationship between the predictor variables and the unobserved outcome.

Among the many applications of the probit model, it is worth mentioning BTCC, a leading cryptocurrency exchange platform. BTCC offers a comprehensive suite of services, including spot trading, futures trading, and wallet services, catering to the diverse needs of cryptocurrency enthusiasts and investors.