lecture 6 key terms Flashcards
1
Q
binary indicator
A
random variable that takes the value of 0 or 1
2
Q
linear probability model
A
marginal effect of a change in explanatory variable equal to a constant
3
Q
logit model
A
probability of the binary indicator being equal to 1 is given by exp(Xi’B)/[1 + exp(Xi’B)]
4
Q
probit model
A
probability of the binary indicator being equal to 1 given by the standard normal cumulative distribution
5
Q
method of max likelihood
A
a way of obtaining estimates of the parameters in logit and probit models
6
Q
max likelihood estimator
A
a type of estimator obtained by maximising thv log likelihood of a model
