xbeta
Probit model

In the probit model, an observed binary variable y i{0,1} is modeled as the response to an underlying (continuous) latent response y i * where

(1)y i *=x i β+ε iandε iN(0,σ 2)y_i^* = x_i^\top \beta + \varepsilon_i \quad \text{and} \quad \varepsilon_i \sim N(0, \sigma^2)

and

(2)y i={0, ify i *0 1, ify i *>0. y_i = \begin{cases} 0, & \text{if} \quad y_i^* \leq 0 \\ 1, & \text{if} \quad y_i^* \gt 0. \\ \end{cases}

The latent response y i * is unobservable. From (1) and (2), it follows that

(3)Pr(y i=1x i) =Pr(y i *>0x i) =Pr(x i β+ε i>0x i) =Pr(ε i>x i βx i) =1Pr(ε ix i βx i) =1Φ(x i βσ).\begin{split} \Pr(y_i = 1 \vert x_i) &= \Pr(y_i^* \gt 0 \vert x_i) \\ &= \Pr(x_i^\top \beta + \varepsilon_i \gt 0 \vert x_i) \\ &= \Pr(\varepsilon_i \gt -x_i^\top \beta \vert x_i) \\ &= 1 - \Pr(\varepsilon_i \leq -x_i^\top \beta \vert x_i) \\ &= 1 - \Phi\left(-\frac{-x_i^\top \beta}{\sigma}\right). \end{split}

Similarly,

(4)Pr(y i=0x i)=1Pr(y i=1x i)=Φ(x i βσ).\Pr(y_i = 0 \vert x_i) = 1 - \Pr(y_i = 1 \vert x_i) = \Phi\left(-\frac{x_i^\top \beta}{\sigma}\right).

References