?===============================================================
? DISCRETE CHOICE MODELING.  Assignment 1, Part I. Binary Choice
? Based on healthcare.lpj
? This symbol:   <***********  shows commands that you must 
? complete with your own specification.
?===============================================================
SAMPLE ; All $
? The years are 1984, 1985, 1986, 1987, 1988, 1991, 1994 $
? Set the sample for the desired year.
INCLUDE  ;  New ; Year = the year you want to analyze $  <***********

? Descriptive statistics for the income variable
DSTAT ; Quantiles ; Rhs = Income $

? How correlated is income with other variables?
REGRESS ; Lhs = Income ; Rhs = one,educ,educ*educ,age,female $

? Male/Female split of the sample
DSTAT ; Rhs = Female $

? Define the variables to be in my model. 
? Should include one, female and educ. Pick some others.
NAMELIST ; xp = one,female,educ , ... $   <***********

? Compare estimates obtained with two different models.
PROBIT  ; Lhs = public ;  Rhs = your specification ; Table = probit $   <***********
LOGIT   ; Lhs = public ;  Rhs = your specification ; Table = logit $   <***********
MAKETABLE ; probit, logit $

? Partial effects for variables in the model
PARTIALS ; Effects:  variable / variable  ; Summary $   <***********
? Obtain partial effects for interaction
LOGIT ; Lhs = public ; Rhs = xp, female*educ ; marginal $
PARTIALS ; Effects : age / married / hsat / female / educ ; summary $

? Structural change test.  Like the Chow test for regression
Model ; For [ (test) Female = *,0,1] ; Lhs = etc. $  <***********

? Delta Method for a nonlinear function
PROBIT ; lhs  = public ; rhs=xp $
WALD ; if [ female=1 ]  
;  parameters = b ; covariance = varb ; labels = kreg_b
; fn1 = n01(b1'xp)/phi(b1'xp) $

? Delta method for another function.  Add this to the WALD command
; fn2 = 1 - fn1 *(fn1 + b1'xp)    <***********

? Try Krinsky and Robb. Add this to the command. Different answer?
	; K&R    <***********

? Bootstrap standard error for IMR function
PROCEDURE  $
PROBIT ;  quietly ; lhs = public ; rhs = xp $
CREATE ;  imr = n01(b'xp)/phi(b'xp) $
CALC   ;  meanimr = female'imr/sum(female) $
ENDPROC $
EXEC ; n=100 ; bootstrap = meanimr  ; histogram $

? Bootstrap to look at behavior of a test statistic.

PROBIT  ; lhs = doctor ; rhs = xp $ (Obtains the 'true' coefficients.)
CREATE  ; ysim = (b'xp + rnn(0,1)) > 0 $ (Simulates the homoscedastic data)
PROBIT  ; Lhs = ysim ; Rhs = xp $ (this computes the restricted estimates)
PROBIT  ; Lhs = ysim ; Rhs = xp ; Het ; Hfn = female ; start = b,0 ; Maxit = 0 $
PROCEDURE $
PROBIT ;  quietly ; lhs=ysim ; rhs = xp $
PROBIT ;  quietly ; lhs=ysim ; rhs=xp
       ;  het ; hfn=female;start=b,0 ; maxit=0 $
ENDPROC $
EXECUTE ; n=100 ; bootstrap = lmstat $
HISTOGRAM; rhs = bootstrp $


? Bootstrap for an estimated vector.

PROCEDURE $
LOGIT ; quiet ; Lhs = doctor ; Rhs = xp ; Prob = p $
CREATE ; scale = p*(1-p) $
CALC ; avgscale = xbr(scale) $
MATRIX ; ape = avgscale * b $
ENDPROC $
EXECUTE ; n = 50 ; bootstrap = ape $

? You can compare your results to the results using the delta method by

LOGIT ; quiet ; Lhs = healthy ; Rhs = xp ; Marginal  $

?===============================================================
? DISCRETE CHOICE MODELING.  Assignment 1.  Panel Data 
? Based on healthdata.lpj  (Subset of Health Care panel)
? This symbol:   <***********  shows commands that you must 
? complete with your own specification.
?===============================================================

SAMPLE ; All $
SETPANEL ; Group = id ; Pds = ti $

? Pooled MLE.  Cluster estimator
NAMELIST ; XP = your list of variables (do not include HEALTHY) $  <*********
PROBIT ; Lhs = doctor ; Rhs = xp $    <*********
PROBIT ; Lhs = doctor ; Rhs = ... ; Cluster = ID $    <*********

? Fixed effects models
NAMELIST ; xfe = your 3 variables $  (for example, age, income, hhkids)   <*********
LOGIT ; Lhs = doctor ; Rhs = xfe ; Panel ; Table = logit_c $
LOGIT ; Lhs = doctor ; Rhs = xfe ; Panel ; FEM ; Table = logit_u $
MAKETABLE ; logit_c,logit_u $

? Random Effects;  Quadrature and Simulation
PROBIT ; Lhs = doctor ; Rhs = one, ... ; Panel ; Random $ (B&M)   <*********
PROBIT ; Lhs = doctor ; Rhs = one, ... ; Panel
	   ; RPM ; Fcn = one(n) ; Halton ; Pts = 50 $   <*********