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Stata operation of the binary selection model and analysis of what the logit model is, many novices are not very clear about this, in order to help you solve this problem, the following editor will explain in detail for you, people with this need can come to learn, I hope you can gain something.

In classical econometrics models, the explained variables are usually assumed to be continuous variables. If the explained variables are discrete variables, such as Yellow1, Magazine, 2, and 3, then they are called discrete selected variable models. The discrete explained variable model is divided into binary selection model and multiple selection model. This paper introduces the stata operation and analysis of binary choice model, namely logit model and probit model.

Calculate the average bias effect and ratio of independent variables

Follow the example in (1), and then do the ratio of the independent variable:

The ratio of white, married and loanprc is obtained by stata:

Logit approve white hrat obrat loanprc unem male married dep sch cosign chist pubrec mortlat1 mortlat2 vr, or nolog

The results are obtained:

According to the results, the odds ratio of white is 0.0828, indicating that whites are 2.5543 times more likely to approve loan applications than non-whites.

Similarly, the forecast probability of married loan application approval is 1.6541 times that of nomarried.

For each unit increase in loanprc, the forecast probability of loan application approval decreases by an average of (1-0.1488)% = 0.8512%.

two

Likelihood ratio Test of logit Model

Likelihood ratio test was used.

First, make a logit model that does not include marital status and race (excluding white and married variables), and save:

Logit approve hrat obrat loanprc unem male dep sch cosign chist pubrec mortlat1 mortlat2 vrestimates store m1

Then make a logit model that includes marital status and race factors, and save:

Logit approve white hrat obrat loanprc unem male married dep sch cosign chist pubrec mortlat1 mortlat2 vrestimates store m2

Then do the likelihood ratio test:

Lrtest m1 m2

The results are obtained:

The original hypothesis is that the two models are equivalent, where the LR test value is 35.66 and the p value is 0.000, which rejects the original hypothesis at a significant level of 5%, indicating that the two models are not equivalent, so marital status and race factors will affect the loan application approval.

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