Discrete Choice Example: Stereotypical Multinomial Logit

Stereotypical Logit Example

This example demonstrates the use of a stereotypical multinomial logit model. It models grade (ABC) achievement rates in a Economics course in relationship to cumulative grade point average (GPA), literacy test score (TUCE), and optional participation in a special economics course (PSI). The first step to setting up all Discrete Choice models is to declare and initialize the dcControl structure:
//Step one: Declare dc control structure
struct dcControl dcCt;

//Initialize dc control structure
dcCt = dcControlCreate();
Next, load and setup the model data using the dcSet procedures:
//Load data
loadm y = aldnel_mat;

//Step two: Describe data names
//Name of dependent variable
dcSetYVar(&dcCt,y[.,1]);
dcsetYLabel(&dcCt,"ABC");

//Y Category Labels
dcSetYCategoryLabels(&dcCt,"A,B,C");

//Name of independent variable
dcSetXVars(&dcCt,y[.,2:4]);
dcsetXLabels(&dcCt,"GPA, TUCE, PSI");
Following data setup, declare the dcOut structure:
//Step three: Declare dcOut struct
struct dcout dcout1;
Finally, call the stereoLogit procedure:
//Step four: Call stereo logit procedure
dcout1 = binaryLogit(dcCt);
call printDCOut(dcOut1);
The example prints the model and data description to screen:
Stereo Logistic Results
                       2015-05-21 07:09:28

Number of Observations:   32
Degrees of Freedom:       26


  1 - A
  2 - B
  3 - C


Distribution Among Outcome Categories For ABC 


Dependent Variable       Proportion  
A                         0.3438     
B                         0.4063     
C                         0.2500     



Descriptive Statistics (N=32):


Independent Vars.          Mean             Std Dev          Minimum          Maximum  
GPA                      3.1172           0.4521           2.0600           4.0000     
TUCE                     21.9375          3.7796           12.0000          29.0000    
PSI                      0.4375           0.4883           0.0000           1.0000     
All coefficients, odds ratios, and marginal effects are printed: In addition a number of summary statistics for model diagnostics are printed:

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