Transcript Logit and Probit Models with Discrete Dependent Variables

Logit and Probit
Models with Discrete Dependent Variables
Why Do We Need A Different Model
Than Linear Regression?
 Appropriate estimation of relations between variables depends on selecting an
appropriate statistical model. There are many different types of estimation
problems in political science.
 Continuous variables where the experiment can be viewed as draws from a
normal distribution.
 Continuous Variables where the experiment is draws from some other
distribution.
 Continuous Variables where the distribution is truncated or censored.
 Discrete Variables - For example, we might model labor force participation,
whether to vote for or against, purchase or not purchase, run for office or not
run for office, etc.
 Models of this type are sometimes called qualitative response models, because the
dependent variables are discrete, rather than continuous. There are several
types of such models including the following.
Type of Qualitative Response Models
 Qualitative dichotomy (e.g., vote/not vote type variables)- We equate "no" with
zero and "yes" with 1. However, these are qualitative choices and the coding of
0-1 is arbitrary. We could equally well code "no" as 1 and "yes" as zero.
 Qualitative multichotomy (e.g., occupational choice by an individual)- Let 0 be a
clerk, 1 an engineer, 2 an attorney, 3 a politician, 4 a college professor, and 5 other.
Here the codings are mere categories and the numbers have no real meaning.
 Rankings (e.g., opinions about a politician's job performance)- Strongly approve
(5), approve (4), don't know (3), disapprove (2), strongly disapprove (1). The
values that are chosen are not quantitative, but merely an ordering of preferences
or opinions. The difference between outcomes is not necessarily the same from 5
to 4 as it is from 2 to 1.
 Count outcomes.
Dichotomous Dependent Variables
 There are various problems associated with estimating a dichotomous
dependent variable under assumptions of a statistical experiment that
draws from a normal distribution, i.e., using regression.

Obviously the statistical experiment is not draws from a normal
distribution, but from something called a Bernoulli distribution. Thus,
estimation is likely to be inefficient. It is also theoretically inconsistent with
the nature of the statistical experiment.
 The dependent variable is discrete and truncated on both ends at 0 and 1.
This leads to a number of other serious problems. Consider first, a graph of
the data in a typical sample of Bernoulli experiments.
Linear Probability Model
1.2
1
0.8
Y, P(Y)
0.6
0.4
0.2
0
-100
-50
-0.2 0
-0.4
X
50
100
 Note that a linear regression line through the actual data cuts through the data
at the point of greatest concentration on each end.
 The residuals from this regression line will only be close to the regression line if
the X variable is also Bernoulli distributed. This means that measures of fit or
hypothesis tests involving the squared errors will be silly. The regression line
will seldom lie near the data.
Linear Probability Model
1.2
1
0.8
Y, P(Y)
0.6
0.4
0.2
0
-100
-50
-0.2 0
-0.4
X
50
100
 Relatedly, this feature also means that the residuals from the linear model will
be dichotomous and heteroskedastic, rather than normal, raising questions
about hypothesis tests.
When y=1, the residual will depend on X and be:
When y=0, the residual will depend on X and be:
 This means that the residuals from the linear probability model will be
heteroskedastic and have a dichotomous character.
 Note that the residuals change systematically with the values of X. This implies
what it termed endogeneity. They are also not distributed normally.
We could "fix" this problem by estimating the linear probability model using
weighted least squares.
However, the problem with this model runs deeper. We must be able to
interpret results from this model as expected values of probabilities. However,
the graph below suggests further problems.
 Observe that some of the probabilities lie above 1 and below zero. This is not
consistent with the rules of probability. We could truncate the model at 0 and 1
to "fix" this problem.
However, note that probability, according to this model, is alleged to change in
linear fashion with changes in X. Yet, this may not be consistent with reality in
many real world situations. For example, consider the probability of home
ownership as a function of income.
Suppose we have prospective buyers with income around 10k per year. If we
change their income by 1k, how much does the probability that they will buy a
home change? Suppose we have prospective buyers with income around 30k. If
we change their income by 1k, how much does the probability that they will
own a home change? Suppose we have prospective buyers with income around
80k. If we change their income by 1k, how much does the probability that they
will own a home change?
 In practice, there are many situations where the probability of a yes outcome
follows an S shaped distribution, rather than the linear distribution alleged by
the linear probability model.
Non-Linear Probability Models
 To begin, assume the appropriate statistical experiment. The statistical
experiment is draws from a Bernoulli distribution. The probability model
from the Bernoulli distribution is given:
f ( y | p)  p yi (1  p)1 yi
where p is a parameter reflecting the probability that y=1.
 The issue then becomes how to specify the probability that y=1. We noted
above that this probability often follows an S shaped distribution. In other
words, the probability that y=1 remains small until some threshold is
crossed, at which point it switches rapidly to remain large after the
threshold. This suggests a cumulative density function.
 Two different cumulative density functions are commonly used in this situation:
the cumulative standard normal distribution (probit) and the cumulative
logistic distribution (logit).

Probit- The cumulative standard normal density is given:
t
P(Y  1) 


1
e
2

Z2
2
dt   ( z )
z  1   2 X ki  ...   k X ki

Logit- The cumulative logistic function for logit is grounded in the concept of
an odds ratio.
Let the log odds that y=1 be given:
 P 
ln 
  1   2 X ki  ...   k X ki  z
1

P


Then solving for the probability that y=1 we have:
P
 ez
1 P
P  (1  P )e z  e z  Pe z
P  Pe z  e z
P (1  e z )  e z
ez
P
1  ez
Probit/Logit
1.2
1
0.8
P(Y)
Probit
0.6
Logit
0.4
0.2
0
-10
0
z
10
 Choosing Between Logit/Probit- In the dichotomous case, there is no basis in
statistical theory for preferring one over the other. In most applications it
makes no difference which one uses.
If we have a small sample the two distributions can differ significantly in their
results, but they are quite similar in large samples.
 Various R2 measures have been devised for Logit and Probit. However, none is a
measure of the closeness of observations to an expected value as in regression
analysis. All are ad hoc.
Hypothesis testing
 t or z test- We can test the significance of the individual coefficients simply
using the point estimates and standard errors (square roots of the diagonal
elements of the asymptotic covariance matrix of estimates). Form a z or t
test by taking
t N k
ˆ k   k 0

s ˆ
k
 Confidence Intervals
Interpretation

Interpreting Dichotomous Logit and Probit
 Coefficients- The actual coefficients in a logit or probit analysis are limited in
their immediate interpretability.
 The signs are meaningful, but the magnitudes may not be, particularly when
the variables are in different metrics.
 Above all, note that you cannot interpret the coefficients directly in terms of
units of change in y for a unit change in x, as in regression analysis.

There are various approaches to imparting substantive meaning into logit and
probit results, including:
 Probability Calculations
 Graphical methods
 First differences
 First Partial derivatives.