📄 to_rlike.m
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function like = to_rlike(b,y,x,a);
% PURPOSE: evaluate tobit log-likelihood
% for right-censoring case (y >= a is censored)
%-----------------------------------------------------
% USAGE: like = to_llike(b,y,x,a)
% where: b = parameter vector (k x 1)
% y = dependent variable vector (n x 1)
% x = explanatory variables matrix (n x m)
% a = right-censoring point (default = 0)
%-----------------------------------------------------
% NOTE: this function returns a scalar equal to the negative
% of the log-likelihood function
% k ~= m because we may have additional parameters
% in addition to the m bhat's (e.g. sigma)
%-----------------------------------------------------
% written by:
% James P. LeSage, Dept of Economics
% University of Toledo
% 2801 W. Bancroft St,
% Toledo, OH 43606
% jlesage@spatial-econometrics.com
% error check
if nargin == 4,
aterm = a;
elseif nargin == 3
aterm = 0;
else
error('wrong # of arguments to to_rlike');
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 = -(aterm-xb)./sqrt(sigma); cdf = .5*(1+erf(xbs./sqrt(2)));
llf2 = log(h+(cdf));
llf = (y < aterm).*llf1 + (y >= aterm).*llf2;
like = -sum(llf);% scalar result
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