function like =  to_liked(b,y,x);% PURPOSE: evaluate tobit log-likelihood%          to demonstrate optimization routines%-----------------------------------------------------% USAGE:    like = to_liked(b,y,x) % where:     b = parameter vector (k x 1)%            y = dependent variable vector (n x 1)%            x = explanatory variables matrix (n x m)%-----------------------------------------------------% NOTE: this function returns a scalar equal to the%       negative of the log likelihood function%       or a scalar sum of the vector depending%       on the value of the flag argument%       k ~= m because we may have additional parameters%           in addition to the m bhat's (e.g. sigma)%-----------------------------------------------------% error checkif nargin ~= 3,error('wrong # of arguments to to_like1'); end;[m1 m2] = size(b);if m1 == 1 b = b';end;   h = .000001;             % avoid sigma = 0   [m junk] = size(b);   beta = b(1:m-1);         % pull out bhat   sigma = max([b(m) h]);   % pull out sigma   xb = x*beta;   llf1 = -(y-xb).^2./(2*sigma) - .5*log(2*pi*sigma);     xbs = xb./sqrt(sigma); cdf = .5*(1+erf(xbs./sqrt(2)));   llf2 = log(h+(1-cdf));   llf = (y > 0).*llf1 + (y <= 0).*llf2;   like = -sum(llf); % scalar result