Maximum Likelihood Estimation in GAUSS
Maximum likelihood is a fundamental workhorse for estimating model parameters with applications ranging from simple linear regression to advanced discrete choice models. Today we learn how to perform maximum likelihood estimation with the GAUSS Maximum Likelihood MT library using our simple linear regression example.
We’ll show all the fundamentals you need to get started with maximum likelihood estimation in GAUSS including:
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- How to create a likelihood function.
- How to call the
maxlikmtprocedure to estimate parameters. - How to interpret the results from
maxlikmt.