Bresnahan Reiss Q2 about comp-econ-sp18 HOT 4 CLOSED

Ok, I get 269.9912536323145. I'll go back into my log_like function and see where I made a mistake.

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Hi @isabelalmazan . Sorry, the bounds should be on the alphas as well as the gammas (not including gamma_L). I am using tuples (0, np.inf) as the bounds on the alphas and gammas. And, for what it's worth, I'm using the basic scipy.optimize.minimize.

Hope this helps!

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I tried your bounds but am still way off:

The closest I've been able to get to the 263 is 261, which I got by "cheating" and using these extra specific bounds:

Even with the results from the cheating/ specific bounds minimization, my np.allclose function returns False at rtol=0.2, so I'm not really sure where I'm going wrong?

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I think the best thing to do to diagnose the problem is to test a few values of the log-likelihood function. We should be getting the exact same numbers. Try the following random test:

theta_test = np.array([1, 1, 1, 1, -1, -0.03,
0.01, -0.02, 0.8, 0.03, 0.1, 0, 0.1, 0.5,
1, 1, 1, 0.1, -1])

neg_log_like(theta_test)

When I run this, I get 324.2397071494959

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