discrete choice modeling william greene stern school of business new york university
TRANSCRIPT
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Discrete Choice Modeling
William Greene
Stern School of Business
New York University
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Modeling Categorical Variables
Theoretical foundations Econometric methodology
Models Statistical bases Econometric methods
Applications
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Binary Outcome
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Multinomial Unordered Choice
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Ordered OutcomeSelf Reported Health Satisfaction
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Categorical Variables Observed outcomes
Inherently discrete: number of occurrences, e.g., family size
Multinomial: The observed outcome indexes a set of unordered labeled choices.
Implicitly continuous: The observed data are discrete by construction, e.g., revealed preferences; our main subject
Implications For model building For analysis and prediction of behavior
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Binary Choice Models
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Agenda
A Basic Model for Binary Choice Specification Maximum Likelihood Estimation Estimating Partial Effects
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A Random Utility Approach
Underlying Preference Scale, U*(choices) Revelation of Preferences:
U*(choices) < 0 Choice “0”
U*(choices) > 0 Choice “1”
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Simple Binary Choice: Insurance
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Censored Health Satisfaction Scale
0 = Not Healthy 1 = Healthy
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Count Transformed to Indicator
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Redefined Multinomial Choice
Fly Ground
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A Model for Binary Choice
Yes or No decision (Buy/NotBuy, Do/NotDo)
Example, choose to visit physician or not
Model: Net utility of visit at least once
Uvisit = +1Age + 2Income + Sex +
Choose to visit if net utility is positive
Net utility = Uvisit – Unot visit
Data: X = [1,age,income,sex]
y = 1 if choose visit, Uvisit > 0, 0 if not.
Random Utility
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Modeling the Binary Choice
Uvisit = + 1 Age + 2 Income + 3 Sex +
Chooses to visit: Uvisit > 0
+ 1 Age + 2 Income + 3 Sex + > 0
> -[ + 1 Age + 2 Income + 3 Sex ]
Choosing Between the Two Alternatives
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Probability Model for Choice Between Two Alternatives
> -[ + 1Age + 2Income + 3Sex ]
Probability is governed by , the random part of the utility function.
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What Can Be Learned from the Data? (A Sample of Consumers, i = 1,…,N)
Are the characteristics “relevant?”
Predicting behavior
- Individual – E.g., will a person visit the physician? Will a person purchase the insurance?
- Aggregate – E.g., what proportion of the population will visit the physician? Buy the insurance?
Analyze changes in behavior when attributes change –E.g., how will changes in education change the proportionwho buy the insurance?
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Application: Health Care UsageGerman Health Care Usage Data (GSOEP), 7,293 Individuals, Varying Numbers of PeriodsData downloaded from Journal of Applied Econometrics Archive. This is an unbalanced panel with 7,293 individuals. They can be used for regression, count models, binary choice, ordered choice, and bivariate binary choice. This is a large data set. There are altogether 27,326 observations. The number of observations ranges from 1 to 7. (Frequencies are: 1=1525, 2=2158, 3=825, 4=926, 5=1051, 6=1000, 7=987).
Variables in the file are DOCTOR = 1(Number of doctor visits > 0) HOSPITAL = 1(Number of hospital visits > 0) HSAT = health satisfaction, coded 0 (low) - 10 (high) DOCVIS = number of doctor visits in last three months HOSPVIS = number of hospital visits in last calendar year PUBLIC = insured in public health insurance = 1; otherwise = 0 ADDON = insured by add-on insurance = 1; otherwise = 0 HHNINC = household nominal monthly net income in German marks / 10000. (4 observations with income=0 were dropped) HHKIDS = children under age 16 in the household = 1; otherwise = 0 EDUC = years of schooling AGE = age in years FEMALE = 1 for female headed household, 0 for male
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Application
27,326 Observations 1 to 7 years, panel 7,293 households observed We use the 1994 year, 3,337 household
observations
Descriptive Statistics=========================================================Variable Mean Std.Dev. Minimum Maximum--------+------------------------------------------------ DOCTOR| .657980 .474456 .000000 1.00000 AGE| 42.6266 11.5860 25.0000 64.0000 HHNINC| .444764 .216586 .340000E-01 3.00000 FEMALE| .463429 .498735 .000000 1.00000
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Binary Choice Data
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An Econometric Model Choose to visit iff Uvisit > 0
Uvisit = + 1 Age + 2 Income + 3 Sex + Uvisit > 0 > -( + 1 Age + 2 Income + 3 Sex)
< + 1 Age + 2 Income + 3 Sex
Probability model: For any person observed by the analyst,
Prob(visit) = Prob[ < + 1 Age + 2 Income + 3 Sex]
Note the relationship between the unobserved and the outcome
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+1Age + 2 Income + 3 Sex
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Modeling Approaches Nonparametric – “relationship”
Minimal Assumptions Minimal Conclusions
Semiparametric – “index function” Stronger assumptions Robust to model misspecification (heteroscedasticity) Still weak conclusions
Parametric – “Probability function and index” Strongest assumptions – complete specification Strongest conclusions Possibly less robust. (Not necessarily)
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Nonparametric Regressions
P(Visit)=f(Income)
P(Visit)=f(Age)
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Klein and Spady SemiparametricNo specific distribution assumed
Note necessary normalizations. Coefficients are relative to FEMALE.
Prob(yi = 1 | xi ) =G(’x) G is estimated by kernel methods
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Fully Parametric
Index Function: U* = β’x + ε Observation Mechanism: y = 1[U* > 0] Distribution: ε ~ f(ε); Normal, Logistic, … Maximum Likelihood Estimation:
Max(β) logL = Σi log Prob(Yi = yi|xi)
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Parametric: Logit Model
What do these mean?
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Parametric vs. Semiparametric
.02365/.63825 = .04133
-.44198/.63825 = -.69249
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Parametric Model Estimation
How to estimate , 1, 2, 3?
It’s not regression The technique of maximum likelihood
Prob[y=1] =
Prob[ > -( + 1 Age + 2 Income + 3 Sex)] Prob[y=0] = 1 - Prob[y=1]
Requires a model for the probability
0 1Prob[ 0] Prob[ 1]
y yL y y
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Completing the Model: F()
The distribution Normal: PROBIT, natural for behavior Logistic: LOGIT, allows “thicker tails” Gompertz: EXTREME VALUE, asymmetric,
Does it matter? Yes, large difference in estimates Not much, quantities of interest are more stable.
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Estimated Binary Choice Models
LOGIT PROBIT EXTREME VALUE
Variable Estimate t-ratio Estimate t-ratio Estimate t-ratio
Constant -0.42085 -2.662 -0.25179 -2.600 0.00960 0.078
Age 0.02365 7.205 0.01445 7.257 0.01878 7.129
Income -0.44198 -2.610 -0.27128 -2.635 -0.32343 -2.536
Sex 0.63825 8.453 0.38685 8.472 0.52280 8.407
Log-L -2097.48 -2097.35 -2098.17
Log-L(0) -2169.27 -2169.27 -2169.27
Ignore the t ratios for now.
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+ 1 (Age+1) + 2 (Income) + 3 Sex
Effect on Predicted Probability of an Increase in Age
(1 is positive)
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Partial Effects in Probability Models
Prob[Outcome] = some F(+1Income…) “Partial effect” = F(+1Income…) / ”x” (derivative)
Partial effects are derivatives Result varies with model
Logit: F(+1Income…) /x = Prob * (1-Prob) Probit: F(+1Income…)/x = Normal density Extreme Value: F(+1Income…)/x = Prob * (-log Prob)
Scaling usually erases model differences
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Estimated Partial Effects
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Partial Effect for a Dummy Variable
Prob[yi = 1|xi,di] = F(’xi+di)
= conditional mean Partial effect of d
Prob[yi = 1|xi,di=1]- Prob[yi = 1|xi,di=0]
Probit: ˆ ˆˆ( ) x xid
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Partial Effect – Dummy Variable
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Computing Partial Effects
Compute at the data means? Simple Inference is well defined.
Average the individual effects More appropriate? Asymptotic standard errors are problematic.
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Average Partial Effects
i i
i ii i
i i
n n
i ii 1 i 1
i
Probability = P F( ' )
P F( ' )Partial Effect = f ( ' ) =
1 1Average Partial Effect = f ( ' )
n n
are estimates of =E[ ] under certain assumptions.
x
xx d
x x
d x
d
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Average Partial Effects vs. Partial Effects at Data Means=============================================Variable Mean Std.Dev. S.E.Mean =============================================--------+------------------------------------ ME_AGE| .00511838 .000611470 .0000106ME_INCOM| -.0960923 .0114797 .0001987ME_FEMAL| .137915 .0109264 .000189
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A Nonlinear Effect
----------------------------------------------------------------------Binomial Probit ModelDependent variable DOCTORLog likelihood function -2086.94545Restricted log likelihood -2169.26982Chi squared [ 4 d.f.] 164.64874Significance level .00000--------+-------------------------------------------------------------Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Mean of X--------+------------------------------------------------------------- |Index function for probabilityConstant| 1.30811*** .35673 3.667 .0002 AGE| -.06487*** .01757 -3.693 .0002 42.6266 AGESQ| .00091*** .00020 4.540 .0000 1951.22 INCOME| -.17362* .10537 -1.648 .0994 .44476 FEMALE| .39666*** .04583 8.655 .0000 .46343--------+-------------------------------------------------------------Note: ***, **, * = Significance at 1%, 5%, 10% level.----------------------------------------------------------------------
P = F(age, age2, income, female)
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Nonlinear Effects
This is the probability implied by the model.
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Partial Effects?----------------------------------------------------------------------Partial derivatives of E[y] = F[*] withrespect to the vector of characteristicsThey are computed at the means of the XsObservations used for means are All Obs.--------+-------------------------------------------------------------Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Elasticity--------+------------------------------------------------------------- |Index function for probability AGE| -.02363*** .00639 -3.696 .0002 -1.51422 AGESQ| .00033*** .729872D-04 4.545 .0000 .97316 INCOME| -.06324* .03837 -1.648 .0993 -.04228 |Marginal effect for dummy variable is P|1 - P|0. FEMALE| .14282*** .01620 8.819 .0000 .09950--------+-------------------------------------------------------------
Separate “partial effects” for Age and Age2 make no sense.They are not varying “partially.”
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Practicalities of Nonlinearities
PROBIT ; Lhs=doctor ; Rhs=one,age,agesq,income,female ; Partial effects $
PROBIT ; Lhs=doctor ; Rhs=one,age,age*age,income,female $PARTIALS ; Effects : age $
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Partial Effect for Nonlinear Terms
21 2 3 4
21 2 3 4 1 2
2
Prob [ Age Age Income Female]
Prob[ Age Age Income Female] ( 2 Age)
Age
(1.30811 .06487 .0091 .17362 .39666 )
[( .06487 2(.0091) ]
Age Age Income Female
Age
Must be computed at specific values of Age, Income and Female
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Average Partial Effect: Averaged over Sample Incomes and Genders for Specific Values of Age
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Interaction Effects
1 2 3
1 2 3 2 3
2
1 3 2 3 3
Prob = ( + Age Income Age*Income ...)
Prob( + Age Income Age*Income ...)( Age)
Income
The "interaction effect"
Prob ( )( Income)( Age) ( )
Income Age
x x x
1 2 3 3 = ( ( ) if 0. Note, nonzero even if 0. x) x
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Partial Effects?
----------------------------------------------------------------------Partial derivatives of E[y] = F[*] withrespect to the vector of characteristicsThey are computed at the means of the XsObservations used for means are All Obs.--------+-------------------------------------------------------------Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Elasticity--------+------------------------------------------------------------- |Index function for probabilityConstant| -.18002** .07421 -2.426 .0153 AGE| .00732*** .00168 4.365 .0000 .46983 INCOME| .11681 .16362 .714 .4753 .07825 AGE_INC| -.00497 .00367 -1.355 .1753 -.14250 |Marginal effect for dummy variable is P|1 - P|0. FEMALE| .13902*** .01619 8.586 .0000 .09703--------+-------------------------------------------------------------
The software does not know that Age_Inc = Age*Income.
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Models for Visit Doctor
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Direct Effect of Age
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Income Effect
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Income Effect on Healthfor Different Ages
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Interaction Effect
2
1 3 2 3 3
1 2 3 3
The "interaction effect"
Prob ( )( Income)( Age) ( )
Income Age
= ( ( ) if 0. Note, nonzero even if 0.
Interaction effect if = 0 is
x x x
x) x
x
3
3
(0)
It's not possible to trace this effect for nonzero Nonmonotonic in x and .
Answer: Don't rely on the numerical values of parameters to inform about
interaction effects. Ex
x.
amine the model implications and the data
more closely.
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Gender – Age Interaction Effects
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Interaction Effects
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Margins and Odds Ratios
Overall take up rate of public insurance is greater for females thanmales. What does the binary choice model say about the difference?
.8617 .9144
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Binary Choice Models
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Average Partial Effects
Other things equal, the take up rate is about .02 higher in female headed households. The gross rates do not account for the facts that female headed households are a little older and a bit less educated, and both effects would push the take up rate up.
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Odds Ratio
Probit and Logit Models
Examine a probability model with one continuous X and one dummy D
Prob(Takeup) F(α+βX+γD)Odds ratio =
1-Prob(Takeup) 1 F(α+βX+γD)
Symmetric Probability Distributions
F(Odds ratio =
α+βX+γD)
F(-α-βX-γD)
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Ratio of Odds Ratios
Probit and Logit Models
F(α+βX+γD)
F(-α-βX-γD)Ratio of Odds Ratios comparing D=1 to D=0 is F(α+βX)
F(-α-βX)
For the probit model, this does not simplify.
For the logit model, the ratio is
exp(α+βX+γD)/[1+exp(α+βX+γD)]1/[1+exp(α+βX+γD)]
exp(α+βX)/[1+exp(α+βX)]
1/[1+exp(α+βX)]
e
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Odds Ratios for Insurance Takeup ModelLogit vs. Probit
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Reporting Odds Ratios
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Margins are about units of measurement
Partial Effect Takeup rate for female
headed households is about 91.7%
Other things equal, female headed households are about .02 (about 2.1%) more likely to take up the public insurance
Odds Ratio The odds that a female
headed household takes up the insurance is about 14.
The odds go up by about 26% for a female headed household compared to a male headed household.