# PS7.5, for the application in Section 7.4
open data7-2   
square EDUC EXPER AGE
# The above generates squares.  The following generate interactions 
genr ED_GEN=EDUC*GENDER
genr ED_RACE=EDUC*RACE
genr ED_CLER=EDUC*CLERICAL
genr ED_MAINT=EDUC*MAINT
genr ED_CRAFT=EDUC*CRAFTS
genr AGE_GEN=AGE*GENDER
genr AGE_RACE=AGE*RACE
genr AGE_CLER=AGE*CLERICAL
genr AGE_MAIN=AGE*MAINT
genr AGE_CRFT=AGE*CRAFTS
genr EXP_GEN=EXPER*GENDER
genr EXP_RACE=EXPER*RACE
genr EXP_CLER=EXPER*CLERICAL
genr EXP_MAIN=EXPER*MAINT
genr EXP_CRFT=EXPER*CRAFTS
genr LWAGE = ln(WAGE)
list
#  The following is the basic model 
ols LWAGE const EDUC EXPER AGE  
genr DFR = $df
genr ut = $uhat
# Auxiliary regression 
ols ut const EDUC EXPER AGE GENDER RACE CLERICAL MAINT CRAFTS sq_EDUC \
 sq_EXPER sq_AGE ED_GEN ED_RACE ED_CLER ED_MAINT ED_CRAFT AGE_GEN \
 AGE_RACE AGE_CLER AGE_MAIN AGE_CRFT EXP_GEN EXP_RACE EXP_CLER EXP_MAIN \
 EXP_CRFT  
# Coefficients etc. for above are same as those in kitchen sink model 
genr DFU = $df
# compute the number of restrictions, LM statistic, and pvalue 
genr NR = DFR - DFU
genr LM = $nrsq
pvalue X NR LM
#  The following is Model 1 in Table 7.5 
ols LWAGE const EDUC EXPER AGE GENDER RACE sq_EDUC sq_EXPER sq_AGE \
 ED_GEN ED_CLER ED_MAINT ED_CRAFT AGE_GEN AGE_RACE AGE_MAIN AGE_CRFT \
 EXP_RACE EXP_CRFT  
# omit variables with insiginficant coefficients, a few at a time 
omit RACE AGE 
omit GENDER 
omit sq_EXPER AGE_GEN  
omit EXP_RACE AGE_RACE  
#  Final model labeled Model 2 in Table 7.5 
omit EXP_CRFT  
# Model 3 in Table 7.4 -- kitchen sink specification 
ols LWAGE const EDUC EXPER AGE GENDER RACE CLERICAL MAINT CRAFTS sq_EDUC \
 sq_EXPER sq_AGE ED_GEN ED_RACE ED_CLER ED_MAINT ED_CRAFT AGE_GEN \
 AGE_RACE AGE_CLER AGE_MAIN AGE_CRFT EXP_GEN EXP_RACE EXP_CLER EXP_MAIN \ 
 EXP_CRFT