In this lecture, I discuss how to use maximum likelihood estimation to estimate the coefficients in a utility model. I start by explaining what a likelihood function is, and then explain general principles of optimization.
Links to Sections:
00:07 - Background on random utility
00:55 - Explaining the likelihood function
04:12 - Practice question 1
05:33 - Maximizing the likelihood function
06:08 - Optimization example - maximizing a profit function
08:42 - Optimality conditions
11:07 - Optimality conditions in single & multiple dimensions
13:02 - Negative null form
13:43 - Analytical optimization
15:11 - Algorithmic optimization
16:48 - Convexity and Concavity
17:37 - Practice question 2
18:05 - Practice question 3
18:33 - Tying it all together: estimating a logit model by maximizing the likelihood
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