Binary Dep for kids Flashcards
What three models do we have?
Logit, Probit, LPM
How do we interpret Binary dependent variable regression
interpreted as a conditional probability function
What is conditional probability
Conditional probability is the probability of one thing being true given that another thing is true
Difference between Probit & Logit and LPM
- Probit, Logit allows for non-linear relationship between dependent and regressors
- Probit, Logit will be between 0 to 1
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R2 Interpretation
No meaningful interpretation
- regression line never able to fit the data perfectly because y is binary and regressors are continious
R2 relies on ___ which make it unusable
linear relationshipt between X and Y
What measures the fit of the model
PseudoR2 measureas the fit using the likelihood function
What is a good PseudoR2 value
Rule of thumb is between 0,2 and 0,4
PsuedoR2 is also called
McFadden
Standard errors in LPM are always
Heteroscedastic, so we use robust standard errors
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When Y is the binary variable -> explain the regression
The population regression function shows the probability that Y = 1 given the value of the regressors
Why is it called LPM
Because the probability that Y = 1 is a linear function of the regressors
What is Probit and Logit regressions
They are regression models that are nonlinear when Y is used as a binary variable
Difference between LPM and Probit & Logit
Probit & Logit regressions ensure that the predicted probability will be between 0 and 1
Probit Regression uses …..
Cumulative Distribution function
What is cumulative distribution function
It is the probability that the variable takes a value less than or equal to X
What does Probit and Logit regression allow for that LPM doesnt
Probit and Logit models allows for non-linear relationship between regressors and dependent variable.
Logit Model uses _________
Logistic cumulative distribution function
Logit and Probit Models are appropriate when attempting to model ___
a dichotomus dependent variable, e.g. yes/no, agree/disagree, like/dislike.
How does the Probit and Logit model look like? Shape
S-Shape, y is between 0 and 1
Y axis shows
We can think of the y-axis as originally having a value 0 to 1. But this value get transformed into the value of log(p/(1.p)). So if p (or y) was 0,5, the new value on this axis is log(0,5/(1-0,5))=0.
when p = 1, we get log(1) - log(0). This equals positive infinity, we now got both positive and negative infinity
where infinity come from
when p = 1, we get log(1) - log(0). This equals positive infinity, we now got both positive and negative infinity