Probit regression model with p = 1 regressor x: Pr(ytxt) = G(BO+ B1xt) where G(30 +...

Transcribed Image Text:

Probit regression model with p = 1 regressor x: Pr(ytxt) = G(BO+ B1xt) where G(30 + B1xt) = O(30 + 31xt) is the Gaussian cdf G(z) when Z~N(0,1); and we observe a sample of data: (y1,x1),(y2,x2),..., (yT,xT). Find the 1st derivatives of the log-likelihood function, i.e.: (i) g(BO); (ii) g(B1). Set these score functions equal to 0. Interpret the meaning or intuition of these equations. Probit regression model with p = 1 regressor x: Pr(ytxt) = G(BO+ B1xt) where G(30 + B1xt) = O(30 + 31xt) is the Gaussian cdf G(z) when Z~N(0,1); and we observe a sample of data: (y1,x1),(y2,x2),..., (yT,xT). Find the 1st derivatives of the log-likelihood function, i.e.: (i) g(BO); (ii) g(B1). Set these score functions equal to 0. Interpret the meaning or intuition of these equations.

Answer rating: 100% (QA)

i gB0 Tt1ytxt GB0 B1xt This equation states that the first derivative of the loglikelihood function ... View the full answer