This homework asks you to think about a relatively simple case of nonlinear optimization: the well known Probit model. Of course there are plenty of canned solutions to this problem, however, here we want to hone our skills a bit when it comes to actually implementing a nonlinear optimization problem. We will see that
providing gradient and/or hessian information to an algorithm changes the speed and quality of convergence
Different algorithms reach slightly different optima
there are several ways to obtain standard errors in a likelihood estimation setting.
Get the notebook here
As usual, teams of at least 2.
submit static html file.
dropbox link via slack.
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