discrete choice modeling

Discrete Choice Modeling

William GreeneStern School of BusinessNew York University

Lab Sessions

Lab Session 2

Analyzing Binary Choice Data

Data Set: Load PANELPROBIT.LPJ

Fit Basic Models

Partial Effects for Interactions

21 2 3 4

1 3 4

2 4

Prob[ 1| ] [ ] [ ]Partial Effects?

[ ]( 2 )

[ ]( )

Compute without extensive additional computation ofe

y x x z x xzA

P A x zxP A xz

xtra variables, etc.

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)20 ; Plot(ci) $

Average Partial Effects---------------------------------------------------------------------Partial Effects Analysis for Probit Probability Function---------------------------------------------------------------------Partial effects on function with respect to AGEPartial effects are computed by average over sample observationsPartial effects for continuous variable by differentiationPartial effect is computed as derivative = df(.)/dx---------------------------------------------------------------------df/dAGE Partial Standard(Delta method) Effect Error |t| 95% Confidence Interval---------------------------------------------------------------------Partial effect .00441 .00059 7.47 .00325 .00557EDUC = 8.00 .00485 .00101 4.80 .00287 .00683EDUC = 10.00 .00463 .00068 6.80 .00329 .00596EDUC = 12.00 .00439 .00061 7.18 .00319 .00558EDUC = 14.00 .00412 .00091 4.53 .00234 .00591EDUC = 16.00 .00384 .00138 2.78 .00113 .00655EDUC = 18.00 .00354 .00192 1.84 -.00023 .00731EDUC = 20.00 .00322 .00250 1.29 -.00168 .00813

Useful Plot

More Elaborate Partial Effects

PROBIT ; Lhs = Doctor ; Rhs = one,age,educ,age^2,age*educ, female,female*educ,income $

PARTIAL ; Effects: income @ female = 0,1 ? Do for each subsample | educ = 12,16,20 ? Set 3 fixed values & age = 20(10)50 ? APE for each setting

Constructed Partial Effects

Predictions List and keep predictions Add ; List ; Prob = PFIT to the probit or logit command (Tip: Do not use ;LIST with large samples!)

Sample ; 1-100 $PROBIT ; Lhs=ip ; Rhs=x1 ; List ; Prob=Pfit $DSTAT ; Rhs = IP,PFIT $

Testing a Hypothesis – Wald Test

SAMPLE ; All $PROBIT ; Lhs = IP ; RHS = Sectors,X1 $MATRIX ; b1 = b(1:3) ; v1 = Varb(1:3,1:3) $MATRIX ; List ; Waldstat = b1'<V1>b1 $CALC ; List ; CStar = CTb(.95,3) $

ˆ ˆ ˆ -1Wald = ( - ) [Est.Var( - )] ( - )

Wald Statistic

β 0 β 0 β 0

Testing a Hypothesis – LM Test

PROBIT ; LHS = IP ; RHS = X1 $PROBIT ; LHS = IP ; RHS = X1,Sectors ; Start = b,0,0,0 ; MAXIT = 0 $

ˆ ˆ

ˆ

ˆ

0 0 -1 0

0

0

LM = ( ) [Est.Hessian ] ( )

=MLE with restrictions imposed

Hessian is computed at .

Lagrange Multiplier Test

g β g β

β

β

Results of an LM test

Maximum iterations reached. Exit iterations with status=1.Maxit = 0. Computing LM statistic at starting values.No iterations computed and no parameter update done.+---------------------------------------------+| Binomial Probit Model || Dependent variable IP || Number of observations 6350 || Iterations completed 1 || LM Stat. at start values 163.8261 || LM statistic kept as scalar LMSTAT || Log likelihood function -4228.350 || Restricted log likelihood -4283.166 || Chi squared 109.6320 || Degrees of freedom 6 || Prob[ChiSqd > value] = .0000000 |+---------+--------------+----------------+--------+---------+----------+|Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | Mean of X|+---------+--------------+----------------+--------+---------+----------+ Constant -.01060549 .04902957 -.216 .8287 IMUM .43885789 .14633344 2.999 .0027 .25275054 FDIUM 2.59443123 .39703852 6.534 .0000 .04580618 SP .43672968 .11922200 3.663 .0002 .07428482 RAWMTL .000000 .06217590 .000 1.0000 .08661417 INVGOOD .000000 .03590410 .000 1.0000 .50236220 FOOD .000000 .07923549 .000 1.0000 .04724409

Note: Wald equaled 163.236.

Likelihood Ratio Test

PROBIT ; Lhs = IP ; Rhs = X1,Sectors $CALC ; LOGLU = Logl $PROBIT ; Lhs = IP ; Rhs = X1 $CALC ; LOGLR = Logl $CALC ; List ; LRStat = 2*(LOGLU – LOGLR) $

Result is 164.878.

LR = 2[LogL(unrestricted) -Logl(restricted)]

Using the Binary Choice Simulator

Fit the model with MODEL ; Lhs = … ; Rhs = …Simulate the model withBINARY CHOICE ; <same LHS and RHS > ; Start = B (coefficients) ; Model = the kind of model (Probit or Logit) ; Scenario: variable <operation> = value / (may repeat) ; Plot: Variable ( range of variation is optional) ; Limit = P* (is optional, 0.5 is the default) $E.g.: Probit ; Lhs = IP ; Rhs = One,LogSales,Imum,FDIum $ BinaryChoice ; Lhs = IP ; Rhs = One,LogSales,IMUM,FDIUM ; Model = Probit ; Start = B ; Scenario: LogSales * = 1.1 ; Plot: LogSales $

Estimated Model for Innovation

+---------+--------------+----------------+--------+---------+----------+|Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | Mean of X|+---------+--------------+----------------+--------+---------+----------+ Index function for probability Constant -1.89382186 .20520881 -9.229 .0000 LOGSALES .16345837 .01766902 9.251 .0000 10.5400961 IMUM .99773826 .14091020 7.081 .0000 .25275054 FDIUM 3.66322280 .37793285 9.693 .0000 .04580618+---------------------------------------------------------+|Predictions for Binary Choice Model. Predicted value is ||1 when probability is greater than .500000, 0 otherwise.||------+---------------------------------+----------------+|Actual| Predicted Value | ||Value | 0 1 | Total Actual |+------+----------------+----------------+----------------+| 0 | 531 ( 8.4%)| 2033 ( 32.0%)| 2564 ( 40.4%)|| 1 | 454 ( 7.1%)| 3332 ( 52.5%)| 3786 ( 59.6%)|+------+----------------+----------------+----------------+|Total | 985 ( 15.5%)| 5365 ( 84.5%)| 6350 (100.0%)|+------+----------------+----------------+----------------+

Effect of logSales on Probability

Model Simulation:

logSales Increases by 10% for all Firms in the Sample+-------------------------------------------------------------+|Scenario 1. Effect on aggregate proportions. Probit Model ||Threshold T* for computing Fit = 1[Prob > T*] is .50000 ||Variable changing = LOGSALES, Operation = *, value = 1.100 |+-------------------------------------------------------------+|Outcome Base case Under Scenario Change || 0 985 = 15.51% 300 = 4.72% -685 || 1 5365 = 84.49% 6050 = 95.28% 685 || Total 6350 = 100.00% 6350 = 100.00% 0 |+-------------------------------------------------------------+

Lab Session 3

Bivariate Extensions of the Probit Model

Bivariate Probit ModelTwo equation modelGeneral usage of

LHS = the set of dependent variables RH1 = one set of independent variables RH2 = a second set of variables

Economical use of namelists is useful here

Namelist ; x1=one,age,female,educ,married,working $Namelist ; x2=one,age,female,hhninc,hhkids $BivariateProbit ;lhs=doctor,hospital

;rh1=x1 ;rh2=x2;marginal effects $

Heteroscedasticity in the Bivariate Probit Model

General form of heteroscedasticity in LIMDEP/NLOGIT: Exponential

σi = σ exp(γ’zi) so that σi > 0

γ = 0 returns the homoscedastic case σi = σ Easy to specify

Namelist ; x1=one,age,female,educ,married,working ; z1 = … $Namelist ; x2=one,age,female,hhninc,hhkids ; z2 = … $BivariateProbit ;lhs=doctor,hospital

;rh1=x1 ; hf1 = z1 ;rh2=x2 ; hf2 = z2$

Heteroscedasticity in Marginal Effects

Univariate case:

If the variables are the same in x and z, these terms are added.

Sign and magnitude are ambiguousVastly more complicated for the bivariate

probit case. NLOGIT handles it internally.

( )

( )

( ) ( )

ii i

i

i i i

i i

i i i i

i i i

E[y | , ] =exp

E[y | , ]exp

E[y | , ]exp exp

β xx zγ z

x z β x βx γ z

x z β x β x γz γ z γ z

Marginal Effects: Heteroscedasticity

+------------------------------------------------------+| Partial Effects for Ey1|y2=1 |+----------+---------------------+---------------------+| | Regression Function | Heteroscedasticity || +---------------------+---------------------+| | Direct | Indirect | Direct | Indirect || Variable | Efct x1 | Efct x2 | Efct h1 | Efct h2 |+----------+----------+----------+----------+----------+| AGE | .00190 | -.00012 | .00000 | .00000 || FEMALE | .10215 | .20688 | -.05880 | -.30944 || EDUC | -.00247 | .00000 | .00000 | .00000 || MARRIED | .00103 | .00000 | .00064 | .00476 || WORKING | -.02139 | .00000 | .00000 | .00000 || HHNINC | .00000 | .00154 | .00000 | .00000 || HHKIDS | .00000 | .00005 | .00000 | .00000 |+----------+----------+----------+----------+----------+

Marginal Effects: Total Effects

+-------------------------------------------+| Partial derivatives of E[y1|y2=1] with || respect to the vector of characteristics. || They are computed at the means of the Xs. || Effect shown is total of 4 parts above. || Estimate of E[y1|y2=1] = .819898 || Observations used for means are All Obs. || Total effects reported = direct+indirect. |+-------------------------------------------++---------+--------------+----------------+--------+---------+----------+|Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | Mean of X|+---------+--------------+----------------+--------+---------+----------+ Constant .000000 ......(Fixed Parameter)....... AGE .00347726 .00022941 15.157 .0000 43.5256898 FEMALE .08021863 .00535648 14.976 .0000 .47877479 EDUC -.00392413 .00093911 -4.179 .0000 11.3206310 MARRIED .00061108 .00506488 .121 .9040 .75861817 WORKING -.02280671 .00518908 -4.395 .0000 .67704750 HHNINC .00216510 .00374879 .578 .5636 .35208362 HHKIDS .00034768 .00164160 .212 .8323 .40273000

Imposing Fixed Value and Equality Constraints

Used throughout LIMDEP in all models,model parameters appear as a long list:

β1 β2 β3 β4 α1 α2 α3 α4 σ and so on. M parameters in total.

Use ; RST = list of symbols for the model parameters, in the right order

This may be used for nonlinear models. Not in REGRESS. Use ;CLS:… for linear models

Use the same name for equal parametersUse specific numbers to fix the values

BivariateProbit ; lhs=doctor,hospital ; rh1=one,age,female,educ,married,working ; rh2=one,age,female,hhninc,hhkids ; rst = beta1,beta2,beta3,be,bm,bw, beta1,beta2,beta3,bi,bk, 0.4 $

--------+-------------------------------------------------------------Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Mean of X--------+------------------------------------------------------------- |Index equation for DOCTORConstant| -1.69181*** .08938 -18.928 .0000 AGE| .01244*** .00167 7.440 .0000 44.3352 FEMALE| .38543*** .03157 12.209 .0000 .42277 EDUC| .08144*** .00457 17.834 .0000 10.9409 MARRIED| .42021*** .03987 10.541 .0000 .84539 WORKING| .03310 .03910 .847 .3972 .73941 |Index equation for HOSPITALConstant| -1.69181*** .08938 -18.928 .0000 AGE| .01244*** .00167 7.440 .0000 44.3352 FEMALE| .38543*** .03157 12.209 .0000 .42277 HHNINC| -.98617*** .08917 -11.060 .0000 .34930 HHKIDS| -.09406** .04600 -2.045 .0409 .45482 |Disturbance correlationRHO(1,2)| .40000 ......(Fixed Parameter)......--------+-------------------------------------------------------------

Miscellaneous Topics

Two Step Estimation Robust (Sandwich) Covariance

matrix Matrix Algebra – Testing for

Normality

Two Step Estimation

Murphy and Topel

This can usually easily be programmed using the models, CREATE, CALC and MATRIX. Several leading cases are built in.

Two Step Estimation: Automated

Application: Recursive Probit

Hospital = bh’xh + c*Doctor + ehDoctor = bd’xd + ed

Sample ; All $Namelist ; xD=one,age,female,educ,married,working ; xH=one,age,female,hhninc,hhkids $Reject ; _Groupti < 7 $Probit ; lhs=hospital;rhs=xh,doctor$Probit ; lhs=doctor;rhs=xd;prob=pd;hold$Probit ; lhs=hospital;rhs=xh,pd;2step=pd$

Robust Covariance Matrix

12ni=1

ML ML

12 2n ni=1 i=1

ML ML ML ML

Standard Covariance Matrix Estimator (General)logLˆ= ˆ ˆ

'Robust' (Sandwich) EstimatorlogL logL logL logˆ= ˆ ˆ ˆ ˆ

V

V

1ni=1

ML ML

Lˆ ˆ

TO WHAT SPECIFICATION 'ERRORS' IS THIS ESTIMATOR ROBUST? IN THE PROBIT CASES THE ESTIMATOR IS INCONSISTENT, SO NOT TO(1) HETEROSCEDASTICITY(2) OMITTED VARIABLES(3) WRONG DIS

TRIBUTIONAL ASSUMPTIONPOSSIBLY TO CROSS OBSERVATION CORRELATION.

Cluster Correction PROBIT ; Lhs = doctor ; Rhs = one,age,female,educ,married,working ; Cluster = ID $

Normal exit: 4 iterations. Status=0. F= 17448.10+---------------------------------------------------------------------+| Covariance matrix for the model is adjusted for data clustering. || Sample of 27326 observations contained 7293 clusters defined by || variable ID which identifies by a value a cluster ID. |+---------------------------------------------------------------------+Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Mean of X--------+------------------------------------------------------------- |Index function for probabilityConstant| -.17336** .08118 -2.135 .0327 AGE| .01393*** .00102 13.691 .0000 43.5257 FEMALE| .32097*** .02378 13.497 .0000 .47877 EDUC| -.01602*** .00492 -3.259 .0011 11.3206 MARRIED| -.00153 .02553 -.060 .9521 .75862 WORKING| -.09257*** .02423 -3.820 .0001 .67705--------+-------------------------------------------------------------

Using Matrix AlgebraNamelists with the current sample serve 2 major functions:

(1) Define lists of variables for model estimation (2) Define the columns of matrices built from the data.

NAMELIST ; X = a list ; Z = a list … $

Set the sample any way you like. Observations are now the rows of all matrices. When the sample changes, the matrices change.

Lists may be anything, may contain ONE, may overlap (some or all variables) and may contain the same variable(s) more than once

Matrix Functions

Matrix Product: MATRIX ; XZ = X’Z $Moments and Inverse MATRIX ; XPX = X’X

; InvXPX = <X’X> $Moments with individual specific weights in variable w. Σi wi xixi’ = X’[w]X. [Σi wi xixi’ ]-1 = <X’[w]X>Unweighted Sum of Rows in a Matrix Σi xi = 1’XColumn of Sample Means (1/n) Σi xi = 1/n * X’1 or MEAN(X) (Matrix function. There are over 100 others.)Weighted Sum of rows in matrix Σi wi xi = 1’[w]X

Normality Test for Probit

i i

i i i i

i i i3 i42

i3 i i4

Testing for normality in the probit model: RHS variables. y = LHS variable

Probit ModelProb[y 1| ] ( ), Normal CDF. ( ) density

[ ,z ,z ], z -(1/3)[( ) 1], z (1

x

x βx βx z x

βx

2i i

ii i i i

i i

1n n n 2i i i i i i i ii=1 i=1 i=1

/4){( )[3 ( ) ]}( ) e y ( ), d

( )[1 ( )]Lagrange Multiplier Statistic. ̂= compute at MLE of

ˆ ˆ ˆˆ ˆˆ ˆ ˆ LM= (ed) d (ed)

βx βxβxx

βx βxβ

z ' z z

Thanks to Joachim Wilde, Univ. Halle, Germany for suggesting this.

Normality Test for Probit

NAMELIST ; XI = One,... $CREATE ; yi = the dependent variable $PROBIT ; Lhs = yi ; Rhs = Xi ; Prob = Pfi $CREATE ; bxi = b'Xi ; fi = N01(bxi) $CREATE ; zi3 = -1/2*(bxi^2 - 1) ; zi4 = 1/4*(bxi*(bxi^2+3)) $NAMELIST ; Zi = Xi,zi3,zi4 $CREATE ; di = fi/sqr(pfi*(1-pfi)) ; ei = yi - pfi

; eidi = ei*di ; di2 = di*di $MATRIX ; List ; LM = 1'[eidi]Zi * <ZI'[di2]Zi> * Zi'[eidi]1 $

Multivariate Probit

MPROBIT ; LHS = y1,y2,…,yM ; Eq1 = RHS for equation 1 ; Eq2 = RHS for equation 2 … ; EqM = RHS for equation M $

Parameters are the slope vectors followed by the lower triangle of the correlation matrix

Constrained Panel Probit

Sample ; 1 - 1270 $MPROBIT ; LHS = IP84, IP85, IP86 ; MarginalEffects ; Eq1 = One,Fdium84,SP84 ; Eq2 = One,Fdium85,SP85 ; Eq3 = One,Fdium86,SP86 ; Rst = b1,b2,b3,b1,b2,b3,b1,b2,b3,r45, r46, r56 ; Maxit = 3 ; Pts = 15 $ (Reduces time to compute)

Estimated Multivariate Probit+---------------------------------------------+| Multivariate Probit Model: 3 equations. || Number of observations 1270 || Log likelihood function -2423.732 || Number of parameters 6 || Replications for simulated probs. = 15 |+---------------------------------------------++---------+--------------+----------------+--------+---------+----------+|Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | Mean of X|+---------+--------------+----------------+--------+---------+----------+ Index function for IP84 Constant .13489406 .03467525 3.890 .0001 FDIUM84 .33571101 .47118274 .712 .4762 .05055702 SP84 .65662961 .13801209 4.758 .0000 .11012047 Index function for IP85 Constant .13489406 .03467525 3.890 .0001 FDIUM85 .33571101 .47118274 .712 .4762 .05051809 SP85 .65662961 .13801209 4.758 .0000 .11014611 Index function for IP86 Constant .13489406 .03467525 3.890 .0001 FDIUM86 .33571101 .47118274 .712 .4762 .05049439 SP86 .65662961 .13801209 4.758 .0000 .11016926 Correlation coefficients R(01,02) .46759312 .03716428 12.582 .0000 R(01,03) .37251014 .03946383 9.439 .0000 R(02,03) .46215054 .03721312 12.419 .0000

Endogenous Variable in Probit Model

PROBIT ; Lhs = y1, y2 ; Rh1 = rhs for the probit model,y2 ; Rh2 = exogenous variables for y2 $

SAMPLE ; All $CREATE ; GoodHlth = Hsat > 5 $PROBIT ; Lhs = GoodHlth,Hhninc ; Rh1 = One,Female,Hhninc ; Rh2 = One,Age,Educ $

Lab Session 4

Panel DataBinary Choice Models with

Panel Data

Telling NLOGIT You are Fitting a Panel Data Model

Balanced Panel Model ; … ; PDS = number of periods $ REGRESS ; Lhs = Milk ; Rhs = One,Labor ; Pds = 6 ; Panel $ (Note ;Panel is needed only for REGRESS)

Unbalanced Panel Model ; … ; PDS = group size variable $ REGRESS ; Lhs = Milk ; Rhs = One,Labor ; Pds = FarmPrds

; Panel $ FarmPrds gives the number of periods, in every period. (More later about unbalanced panels)

Group Size Variables for Unbalanced Panels

Farm Milk Cows FarmPrds1 23.3 10.7 31 23.3 10.6 31 25 9.4 32 19.6 11 22 22.2 11 23 24.7 11 43 25.4 12 43 25.3 13.5 43 26.1 14.5 44 55.4 22 24 63.5 22 2

Application to Spanish Dairy Farms Dairy.lpj

Input Units Mean Std. Dev. Minimum Maximum

Milk Milk production (liters) 131,108 92,539 14,110 727,281

Cows # of milking cows 2.12 11.27 4.5 82.3

Labor # man-equivalent units 1.67 0.55 1.0 4.0

Land Hectares of land devoted to pasture and crops.

12.99 6.17 2.0 45.1

Feed Total amount of feedstuffs fed to dairy cows (tons)

57,941 47,981 3,924.14 376,732

N = 247 farms, T = 6 years (1993-1998)

Global Setting for Panels

SETPANEL ; Group = the name of the ID variable ; PDS = the name of the groupsize variable to create $

Subsequent model commands state ;PANEL with no other specifications requred to set the panel.Some other specifications usually required for thespecific model – e.g., fixed vs. random effects.

Dialog Boxes for Model Commands

Selecting PANEL from the Options Tab

Load the Probit Data Set

Data for this session are PANELPROBIT.LPJ

Various Fixed and Random Effects ModelsRandom ParametersLatent Class

Fixed Effects Models

? Fixed Effects Probit. ? Looks like an incidental parameters problem.Sample ; All $Namelist ; X = IMUM,FDIUM,SP,LogSales $Probit ; Lhs = IP ; Rhs = X ; FEM ; Marginal ; Pds=5 $Probit ; Lhs = IP ; Rhs = X,one ; Marginal $

Logit Fixed Effects Models Conditional and Unconditional FE

? Logit, conditional vs. unconditionalLogit ; Lhs = IP ; Rhs = X ; Pds = 5 $ (Conditional)Logit ; Lhs = IP ; Rhs = X ; Pds = 5 ; Fixed $

Hausman Test for Fixed Effects

? Logit: Hausman test for fixed effects?Logit ; Lhs = IP ; Rhs = X ; Pds = 5 $Matrix ; Bf = B ; Vf = Varb $Logit ; Lhs = IP ; Rhs = X,One $Calc ; K = Col(X) $Matrix ; Bp = b(1:K) ; Vp = Varb(1:K,1:K) $Matrix ; Db = Bf - Bp ; DV = Vf - Vp ; List ; Hausman = Db'<DV>Db $Calc ; List ; Ctb(.95,k) $

Random Effects and Random Constant

? Random effects? Quadrature Based (Butler and Moffitt) EstimatorProbit ; Lhs = IP ; Rhs = X,One ; Random ; Pds = 5 $Calc ; List ; RhoQ = rho $? Simulation Based EstimatorProbit ; Lhs = IP ; Rhs = X,one ; RPM ; Pds = 5 ; Fcn = One(N) ; Halton ; Pts = 25 $Calc ; List ; RhoRP = b(6)^2/(1+b(6)^2) ; RhoQ $

Unbalanced Panel Data SetLoad healthcare.lpjCreate group size variableExamine Distribution of Group Sizes

Sample ; all$Setpanel ; Group = id ; Pds = ti $Histogram ; rhs = ti $

Group Sizes

A Fixed Effects Probit Model

Probit ;lhs=doctor ; rhs=age,hhninc,educ,married ; fem ; panel ; Parameters $+---------------------------------------------+| Probit Regression Start Values for DOCTOR || Maximum Likelihood Estimates || Dependent variable DOCTOR || Weighting variable None || Number of observations 27326 || Iterations completed 10 || Log likelihood function -17700.96 || Number of parameters 5 || Akaike IC=35411.927 Bayes IC=35453.005 || Finite sample corrected AIC =35411.929 |+---------------------------------------------++---------+--------------+----------------+--------+---------+----------+|Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | Mean of X|+---------+--------------+----------------+--------+---------+----------+ AGE .01538640 .00071823 21.423 .0000 43.5256898 HHNINC -.09775927 .04626475 -2.113 .0346 .35208362 EDUC -.02811308 .00350079 -8.031 .0000 11.3206310 MARRIED -.00930667 .01887548 -.493 .6220 .75861817 Constant .02642358 .05397131 .490 .6244

These are the pooled data estimates used to obtain starting values for the iterations to get the full fixed effects model.

Fixed Effects Model

Nonlinear Estimation of Model ParametersMethod=Newton; Maximum iterations=100Convergence criteria: max|dB| .1000D-08, dF/F= .1000D-08, g<H>g= .1000D-08Normal exit from iterations. Exit status=0.+---------------------------------------------+| FIXED EFFECTS Probit Model || Maximum Likelihood Estimates || Dependent variable DOCTOR || Number of observations 27326 || Iterations completed 11 || Log likelihood function -9454.061 || Number of parameters 4928 || Akaike IC=28764.123 Bayes IC=69250.570 || Finite sample corrected AIC =30933.173 || Unbalanced panel has 7293 individuals. || Bypassed 2369 groups with inestimable a(i). || PROBIT (normal) probability model |+---------------------------------------------++---------+--------------+----------------+--------+---------+----------+|Variable | Coefficient | Standard Error |b/St.Er.|P[|Z|>z] | Mean of X|+---------+--------------+----------------+--------+---------+----------+ Index function for probability AGE .06334017 .00425865 14.873 .0000 42.8271810 HHNINC -.02495794 .10712886 -.233 .8158 .35402169 EDUC -.07547019 .04062770 -1.858 .0632 11.3602526 MARRIED -.04864731 .06193652 -.785 .4322 .76348771

Computed Fixed Effects Parameters

Lab Session 5

Modeling Heterogeneity with Random Parameters and Latent Classes

Random Parameters Model? Random parameters specification?Logit ; Lhs = IP ; Rhs = One,IMUM,FDIUM,SP,LogSales ; Pds = 5 ; RPM ; Halton ; Pts = 25 ; Cor ; Fcn = One(n),IMUM(n),FDIUM(n) ; Marginal ; Parameters $Sample ; 1 - 1270 $Create ; bimum = 0 $Matrix ; bi = beta_i(1:1270,2:2) $Create ; bimum = bi $Kernel ; Rhs = bimum $

BIMUM

.84

1.68

2.52

3.36

4.20

.001.2000 1.2750 1.3500 1.4250 1.5000 1.57501.1250

Kernel density estimate for BIMUM

Dens

ity

Random Parameters with Industry Heterogeneity

? Random parameters with industry heterogeneity? Examine effect of industry heterogeneity.Sample ; All $Logit ; Lhs = IP ; Rhs = One,IMUM,FDIUM,SP,LogSales ; Pds = 5 ; RPM = InvGood,RawMtl ; Halton ; Pts = 15 ; Cor ; Fcn = One(n),IMUM(n),FDIUM(n) ; Marginal ; Parameters $Create; Bimum = beta_i(firm,2) $Regress ; Lhs = Bimum ; Rhs = one,InvGood,RawMtl $

Latent Class Models

? Latent class modelsSample ; All $Logit ; Lhs = IP ; Rhs = X ; LCM ; Pds=5 ; Pts = 3 $Logit ; Lhs = IP ; Rhs = X ; LCM=Invgood,Rawmtl ; Pds=5 ; Pts = 3 $Logit ; Lhs = IP ; Rhs = X ; LCM ; Pds=5 ; Pts = 4 $Logit ; Lhs = IP ; Rhs = X ; LCM ; Pds=5 ; Pts = 5 $

discrete choice modeling

Documents

age educ

subsample educ

average partial effects

ip rhs

agepartial effects

effects analysis

hypothesis lm test probit

doctor rhs