1. Empirical Methods for Microeconomic Applications
William Greene Department of Economics Stern School of Business

2. Upload Your Project File

3. Probit Model Command
Load healthcare.lpj
Command Builder
Text Editor

4. Command Builder
Go button in command builder

8. Partial Effects for Interactions

9. Partial Effects Build the interactions into the model statement
PROBIT ; Lhs = Doctor ; Rhs = one,age,educ,age^2,age*educ $
Built in computation for partial effects
PARTIALS ; Effects: Age & Educ = 8(2) ; Plot(ci) $

10. Average Partial Effects
Partial Effects Analysis for Probit Probability Function
Partial effects on function with respect to AGE
Partial effects are computed by average over sample observations
Partial effects for continuous variable by differentiation
Partial effect is computed as derivative = df(.)/dx
df/dAGE Partial Standard
(Delta method) Effect Error |t| 95% Confidence Interval
Partial effect
EDUC =
EDUC =
EDUC =
EDUC =
EDUC =
EDUC =
EDUC =

11. Useful Plot

12. More Elaborate Partial Effects
PROBIT ; Lhs = Doctor ; Rhs = one,age,educ,age^2,age*educ, female,female*educ,income $
PARTIAL ; Effects: income @ female = 0, ? Do for each subsample | educ = 12,16,20 ? Set 3 fixed values & age = 20(10)50 ? APE for each setting

13. Constructed Partial Effects

14. PROBIT ; Lhs=doctor ; Rhs=… ; List ; Prob=Pfit $
Predictions
List and keep predictions
Add ; List ; Prob = PFIT
to the probit or logit command
(Tip: Do not use ;LIST with large samples!)
Sample ; $
PROBIT ; Lhs=doctor ; Rhs=…
; List ; Prob=Pfit $
DSTAT ; Rhs = Doctor,PFIT $

15. Lab Session 2
Ordered Choice Models

16. Data Set healthcare.lpj Data for this session are
Refer to healthcare.lim for full list of the variables.
This is an unbalanced panel. The group counter is already in the data set. Use ;PDS=_Groupti for panel models

17. Binary Dependent Variables
DOCTOR = visited the doctor at least once
HOSPITAL = went to the hospital at least once.
PUBLIC = has public health insurance (1=YES)
ADDON = additional health insurance.(1=Yes)
ADDON is extremely unbalanced.

18. Dependent Variables: Ordered
HSAT = ordered reported health satisfaction, coded 0,1,…,10.
Use with ORDERED
or ORDERED ; Logit
Request marginal effects with
; Marginal
as usual.

19. Ordered Choice Models Ordered ; Lhs = dependent variable
; Rhs = One, … independent variables $
Remember to include the constant term
For ordered logit in stead of ordered probit, use
Ordered ; Logit ; Lhs = dependent variable
To get marginal effects, use ; Margin as usual.
There are fixed and random effects estimators for this model:
; FEM ; PDS = _Groupti
; Random ; PDS = _Groupti

20. Sample Selection in Ordered Choice

21. Sample Selection Ordered Probit
PROBIT ; Lhs = … ; Rhs = … ; HOLD $
ORDERED ; Lhs = … ; Rhs = … ; Selection $
This is a maximum likelihood estimator, not a least squares estimator. There is no ‘lambda’ variable. The various parameters are present in the likelihood function.

22. Zero Inflated Ordered Probit

23. Zero Inflated Ordered Probit Model
Zero inflated ordered probit model with correlation:
A probit model for the zero cell
(E.g., You can use DOCTOR for a model.)
Create ; y1 = y > 0 $
Probit ; … ; HOLD $
Ordered probit with excess zeros
Orde ; Lhs … ; Rhs … ; ZIOP$
Correlation between w (in probit) and ε in ordered probit model
; CORRELATION is optional. Rho=0 is the default.

24. Hierarchical Ordered Probit
Hierarchical ordered probit. Ordered probit in which threshold parameters depend on variables. Two forms:
HO1: μ(i,j) = exp[θ(j) + δ’z(i)].
HO2, different δ vector for each j.
Use ORDERED ; … ; HO1 = list of variables or
ORDERED ; … ; HO2 = list of variables.
Can combine with SELECTION models and zero inflation models.
This is also the Pudney and Shields generalized ordered probit from Journal of Applied Econometrics, August 2000, with the modification of using exp(…) and internally, a way to make sure that the thresholds are ordered..

25. Lab Session 2
Models for Counts

26. Data Set healthcare.lpj Data for this session are
Refer to healthcare.lim for full list of the variables.
This is an unbalanced panel. The group counter is already in the data set. Use ;PDS=_Groupti for panel models

27. Dependent Variables: Count
DOCVIS = count of visits to the doctor
HOSPVIS = count of visits to the hospital.
There are outliers. It helps to use truncated or censored samples
(1) Truncated Data
SAMPLE ; All $
REJECT ; DocVis > 10 $ before using or
REJECT ; HospVis > 10 $ before using.
Then, if using a panel data estimator, use
REGRESS ; Lhs = One ; Rhs = One ; Str = ID ; Panel $
to create the _GROUPTI count variable
(2) Censored Data
CREATE ; DocVis10 = Min(10,DocVis) ; Hosp10 = Min(10,HospVis) $

28. Models for Count Data Basic models Poisson and negative binomial
POISSON ; Lhs = y ; Rhs = One,… $
NEGBIN ; Lhs = y ; Rhs = One,… $
Many extensions
Various heterogeneity forms
Panel data
Random parameters and latent class
Zero inflation
Sample selection
Censoring and truncation
Numerous others… (some, far from all, shown below)

29. Robust Covariance Matrix
“Robust” sandwich estimator is appropriate for the Poisson and other loglinear models
POISSON ; … ; ROBUST $

30. “Offset” Variables Poisson (and NB) mean is a rate per unit of time.
In the sample, all observations should be observed (exposed) for the same length of time.
Else, the appropriate model is as shown below.

31. Exposure Variable

32. Sample Selection

33. Estimating a Selection Model
? Selection Equation
Probit ; Lhs = … ; Rhs = …
; Hold $
? Main Regression Equation
Poisson ; Lhs = … ; Rhs = …
; Selection $

34. NegBin with Heterogeneity in Alpha
In the negative binomial model, the overdispersion parameter is α, with the model assumption
λ = exp(β’x)
E[y|x] = λ
Var[y|x] = λ[1+ α λ]
We allow α to be heterogeneous:
α = exp(δ’z)
Use ; Hfn = … variables in z

35. Censoring and Truncation
Censoring limit C
Right (upper): Values larger than C are set to Ci. The largest value in the sample is C. Use ;LIMIT=C;MAXIMUM
Left (lower): Values less than C are set equal to C. The smallest value in the sample is C. Use ;LIMIT=C
Truncation limit C
Right (upper): Values greater than or equal to C have been discarded. The largest value in the sample is C-1. Use ;LIMIT=C;TRUNCATION;UPPER
Left (lower): Values less than or equal to C have been discarded. The smallest value in the sample is C+1. Use ;LIMIT=C;TRUNCATION

36. Zero Inflation Two regime, latent class model Reduced form:
Prob[Regime 1 => y=0] = q
Prob[y = j|Regime 2] = Poisson or NegBin, λ=exp(β’x)
Reduced form:
Prob[y=0] = q + (1-q)P(0)
Prob[y=j > 0] = (1-q)P(j)
Regime Models:
q = Probit or Logit
Structures:
ZIP: Probit or Logit F(γ’z) z can be any set of variables
ZIP-tau: Probit or Logit F(τ β’x) – same β’x as above

37. ZIP and ZIP-tau Models ;ZIP Logit, ZIP-tau
;ZIP = Normal: Probit ZIP-tau
;ZIP [=Normal] ; Rh2 = variables in z
Alternative Models
Default is Poisson
;MODEL = NegBin
;MODEL = Gamma

38. Hurdle Model Two Part Model:
Prob[y=0] = Logit or Probit using β’x from the count model or γ’z as specified with ;RH2=list
Prob[y=j|j>0] = Truncated Poisson or NegBin
Two part decision: Drug or alcohol use, for example
POISSON ; … ; Hurdle $
POISSON ; … ; Hurdle ; Rh2 = List $

39. Poisson and NB with Normal Heterogeneity

40. Fixed and Random Effects
Poisson or NegBin
;PDS=setting
Fixed Effects
Default is a conditional esitmator
;FEM uses the unconditional estimator
The two are algebraically identical but use different algorithms
Random Effects
Use ; RANDOM
Can be fit as a random parameters model with just a random constant

41. Random Parameters Poisson ; LHS = dependent variable
; RHS = independent variable(s)
; PDS = setting (may be ;PDS=1)
; RPM ; PTS = number of Points
; Halton (for smarter integration method)
; Correlated if desired to fit correlated
parameters model
; FCN = variable(n) , variable(n), …
to indicate which parameters are random

42. Latent Class Poisson (or NEGBIN) ; LHS = dependent variable
; RHS = independent variable(s)
; LCM for a latent class model
; LCM = variables if probabilities are heterogeneous
; PDS = setting (may be ;PDS=1)
; PTS = number of latent classes