# Lecture 21

```Econ 140
Binary Response
Lecture 21
Lecture 21
1
Today’s plan
Econ 140
• Three models:
• Linear probability model
• Probit model
• Logit model
• L21.xls provides an example of a linear probability model
and a logit model
Lecture 21
2
Discrete choice variable
• Defining variables:
Yi = 1 if individual :
Takes BART
Joins a union
Econ 140
Yi = 0 if individual:
Does not take BART
Does not join a union
• The discrete choice variable Yi is a function of individual
characteristics: Yi = a + bXi + ei
Lecture 21
3
Graphical representation
Econ 140
X = years of labor market experience
Y = 1 [if person joins union]
= 0 [if person doesn’t join union]
Y
1
Yˆ
Observed data with OLS
regression line
0
Lecture 21
X
4
Linear probability model
Econ 140
• The OLS regression line in the previous slide is called the
linear probability model
– predicting the probability that an individual will join a
union given their years of labor market experience
• Using the linear probability model, we estimate the
equation:
Yˆ  aˆ  bˆX
– using aˆ &amp; bˆ
Lecture 21
we can predict the probability
5
Linear probability model (2)
Econ 140
• Problems with the linear probability model
1) Predicted probabilities don’t necessarily lie within the 0
to 1 range
2) We get a very specific form of heteroskedasticity
• errors for this model are ei  Yi  Yˆi
• note: Yˆi values are along the continuous OLS line,
but Yi values jump between 0 and 1 - this creates
large variation in errors
3) Errors are non-normal
• We can use the linear probability model as a first guess
– can be used for start values in a maximum likelihood
Lecture 21problem
6
Econ 140
• Suggestion: curve that runs strictly between 0 and 1 and
tails off at the boundaries like so:
Y
1
0
Lecture 21
7
Econ 140
• Recall the probability distribution function and cumulative
distribution function for a standard normal:
1
PDF
0
Lecture 21
0
CDF
8
Probit model
Econ 140
• For the standard normal, we have the probit model using
the PDF
• The density function for the normal is:
1
 1 2
f Z  
exp  Z 
2
 2 
where Z = a + bX
• For the probit model, we want to find
Pr(Yi  1)  F Z i 
f Z i   PDF , F ( Z i )  CDF
Pr(Z  z )  CDF
Lecture 21
9
Probit model (2)
Econ 140
• The probit model imposes the distributional form of the
CDF in order to estimate a and b
• The values aˆ and bˆ have to be estimated as part of the
maximum likelihood procedure
Lecture 21
10
Logit model
Econ 140
• The logit model uses the logistic distribution
Density:
1
ez
gz  
1 ez
Cumulative:
1
G z  
1  ez
Standard normal F(Z)
Logistic G(Z)
0
Lecture 21
11
Maximum likelihood
Econ 140
• Alternative estimation that assumes you know the form of
the population
• Using maximum likelihood, we will be specifying the
model as part of the distribution
Lecture 21
12
Maximum likelihood (2)
Econ 140
• For example: Bernoulli distribution where: (with a
parameter )
Pr(Y  1)  
Pr(Y  0)  1  
• We have an outcome
1110000100
• The probability expression is:
 3 1   4 1   2   4 1   6
  0 .4
• We pick a sample of Y1….Yn
PrYi  1  
PrYi  0   1  
Lecture 21
13
Maximum likelihood (3)
Econ 140
• Probability of getting observed Yi is based on the form
we’ve assumed:
 Yi 1   1Yi 
• If we multiply across the observed sample:
n

  Yi 1   (1Yi )
i 1

• Given we think that an outcome of one occurs r times:


( nr )
r
ˆ
ˆ
 1
Lecture 21
14
Maximum likelihood (3)
• If we take logs, we get


Econ 140

L ˆ  r log ˆ  n  r log 1  ˆ

– This is the log-likelihood
– We can differentiate this and obtain a solution for ˆ
Lecture 21
15
Maximum likelihood (4)
Econ 140
• In a more complex example, the logit model gives
PrYi  1  G Z i 
Z i  a  bX i
PrYi  0   1  G Z i 
• Instead of looking for estimates of  we are looking for
estimates of a and b
• Think of G(Zi) as :
– we get a log-likelihood
L(a, b) = Si [Yi log(Gi) + (1 - Yi) log(1 - Gi)]
– solve for a and b
Lecture 21
16
Example
Econ 140
• Data on union membership and years of labor market
experience (L21.xls)
• To build the maximum likelihood form, we can think of:
– intercept: a
– coefficient on experience : b
• There are three columns
– Predicted value Z
– Estimated probability(on the CDF)
– Estimated likelihood as given by the model
• The Solver from the Tools menu calculates estimates of a
and b
Lecture 21
17
Example (2)
Econ 140
• How the solver works:
• Defining a and b using start values
• Choose start values of a and b equal to zero
• Define our model: Z = a + bX
1
• Define the predictive possibilities:
G z  
1  ez
• Define the log-likelihood and sum it
– Can use Solver to change the values on a and b
Lecture 21
18
Comparing parameters
Econ 140
• How do we compare parameters across these models?
• The linear probability form is: Y = a + bX
– where  Pr
b
X
• Recall the graphs associated with each model
– Consequently  Pr
 g Zˆ i   b
X
– This is the same for the probit and logit forms
Lecture 21
19
L21.xls example
Econ 140
• Predicting the linear probability model:
Uˆ  0.281  0.005EXPER
• Note the value of the estimated coefficient (b) = 0.005
• For the logit form:
– use logit distribution:
ez
gz  
1 ez
– logit estimated equation is:
Z = U = -0.923 + 0.020EXPER
Lecture 21
20
L21.xls example (2)
Econ 140
• At 20 years of experience:
Z = U = -0.923 + 0.020(20) = -0.523
eZ = e-0.523 = 0.590
g(Z) = (0.590/(1+0.590)) = 0.371
• Thus the slope at 20 years of experience is:
0.371 x 0.020 = 0.007
• Note the similarity (OLS value = 0.005), but for other
examples the difference can be notable.
• Most software (e.g. STATA) will give the coefficient from
the logit, or the differential slope.
Lecture 21
21
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