Lecture 7: Limited Dependent Variables and Maximum Likelihood Flashcards
What is the Likelihood Function
What is the Log likelihood function?
What is the Maximum Likelihood estimator?
What are the two different types of discrete variables?
What are the three different types of binary models?
Describe the linear probability model
What are the three advantages of the linear probability model
What are the 2 disadvantages of the linear probability model
What are the solutions to the disadvantahes of the linear probability model?
Explain the probit model
Explain the Logit model
Explain marginal effects for binary models. How does it differ for linear vs non-linear models?
What is the odds ratio and the log-odds ratio?
What are the two methods for looking at goodness of fit for a nonlinear model?
Explain the 4 steps for the Percentage correctlu predicted Gooness of fit test
Explain the Pseudo R-squred goodness-of-fit test
Should we use LPM or probit/logit models?
Explain what censored models are
Explain what Truncated models are
What happens if we do OLS estimation on censored or tuncated data?
How does Maximum likelihood (ML) estimation address censoring and tuncation?
How does censored ML esatimation work?
What are selection models?
Describe the set up on the selction problem for wages
Explain the Heckman 2 stage selection model?
What exclusion restrictions are required for the Heckman selection model?
