TERM 1
Limited Dependent Variable
DEFINITION 1
A dependent variable that can take on only a limited set of
values.For example, the variable might be a 021 binary
variable or arise from one of the models described in
Appendix 11.3.
TERM 2
Linear probability model
DEFINITION 2
A regression model in which Y is a b inary variable.(From web) In
statistics, a linear probability model is a special case of a binomial
regression model. Here the observed variable for each observation
takes values which are either 0 or 1. The probability of observing a
0 or 1 in any one case is treated as de pending on one or more
explanatory variables.
TERM 3
Probit
(regression)
DEFINITION 3
A nonlinear regression model for a binary dependent variable
in which the population regression function is modeled using
the cumulative standard normal distribution function.(From
web) In statistics, a probit model is a type of regression
where the dependent variable can only take two values, for
example married or not married.
TERM 4
Logit
(regression)
DEFINITION 4
A nonlinear regression model for a bin ary dependent variable in
which the population regression func tion is modeled using the
cumulative logistic distribution functio n.(From web) In statistics,
logistic regression is a type of regressio n analysis used for
predicting the outcome of a categor ical dependent variable based
on one or more predictor variables.
TERM 5
Nonlinear least squares estimator
DEFINITION 5
The estimator obtained by minimizing the sum of squared
residuals when the regression function is nonlinear in the
parameters.
