The document provides a history and overview of the linear probit model. It was first proposed in 1935 and became practical in the 1970s with mainframe computers. The probit model is used to model dichotomous dependent variables through a linear combination of continuous explanatory variables. It represents the probability that a binary response variable equals 1 given the value of regressors. An example is given of factors that may influence whether a political candidate wins an election.

1. LINEAR PROBIT MODEL

2. HISTORY
 The probit model was first proposed by Chester Bliss in 1935.
 Estimation of the model became practical in the 1970s with the availability of
mainframe computers which could solve maximization problems.

3. LINEAR PROBIT MODEL
 Dependent dummy variable is a binary variable.
 Here explanatory variable is a continuous variable.
Y(1,0)=f(x1,x2,….,xn)
If dependant variable is a binary variable and explanatory variable is a
continuous variable, then it is known as linear probability model.

4.  For example Y may represent presence/absence of a certain condition,
success/failure of some device, answer yes/no on a survey, etc. We also have a
vector of regressors X, which are assumed to influence the outcome Y.

5. PROBIT MODEL
 Probit- PRObability + unIT
 It is a type of dependent dummy variable.
 It is a binary response model.
 Probit model is used to model dichotomous or binary outcome variables
i.e., bivariate(0 or 1).

6. Example
 Suppose that we are interested in the factors that influence whether a political
candidate wins an election. The outcome (response) variable is binary (0/1); win or
lose. The predictor variables of interest are the amount of money spent on the
campaign, the amount of time spent campaigning negatively and whether the
candidate is an incumbent.

7. PROBIT MODEL

9. THANK YOU…