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* A small do-file to calculate AIC and SIC in STATA 6.0
* use "C:\Econ472\auto2.dta"
* gen t=_n
* label variable t "Integer time period"
* tsset t
*
* Model 1.1: Full Model
regress  gas  L.gas  LD.gas  price  D.price  LD.price  income  D.income  LD.income
matrix   b1=get(_b)
scalar   AIC1=log(_result(4)/_result(1))+(colsof(b1)/_result(1))*2
scalar   SIC1=log(_result(4)/_result(1))+(colsof(b1)/_result(1))*log(_result(1))
*
* Model 1.2: Drop LD.income
regress  gas  L.gas  LD.gas  price  D.price  LD.price  income  D.income
matrix   b2=get(_b)
scalar   AIC2=log(_result(4)/_result(1))+(colsof(b2)/_result(1))*2
scalar   SIC2=log(_result(4)/_result(1))+(colsof(b2)/_result(1))*log(_result(1))
*
* Model 1.3: Drop LD.price
regress  gas  L.gas  LD.gas  price  D.price            income  D.income  LD.income
matrix   b3=get(_b)
scalar   AIC3=log(_result(4)/_result(1))+(colsof(b3)/_result(1))*2
scalar   SIC3=log(_result(4)/_result(1))+(colsof(b3)/_result(1))*log(_result(1))
*
* Model 1.4: Drop LD.gas
regress  gas  L.gas          price  D.price  LD.price  income  D.income  LD.income
matrix   b4=get(_b)
scalar   AIC4=log(_result(4)/_result(1))+(colsof(b4)/_result(1))*2
scalar   SIC4=log(_result(4)/_result(1))+(colsof(b4)/_result(1))*log(_result(1))
*
* Model 1.5: Drop LD.price, LD.income
regress  gas  L.gas  LD.gas  price  D.price            income  D.income 
matrix   b5=get(_b)
scalar   AIC5=log(_result(4)/_result(1))+(colsof(b5)/_result(1))*2
scalar   SIC5=log(_result(4)/_result(1))+(colsof(b5)/_result(1))*log(_result(1))
*
* Model 1.6: Drop LD.gas, LD.income
regress  gas  L.gas          price  D.price  LD.price  income  D.income 
matrix   b6=get(_b)
scalar   AIC6=log(_result(4)/_result(1))+(colsof(b6)/_result(1))*2
scalar   SIC6=log(_result(4)/_result(1))+(colsof(b6)/_result(1))*log(_result(1))
*
* Model 1.7: Drop LD.gas, LD.price
regress  gas  L.gas          price  D.price            income  D.income  LD.income
matrix   b7=get(_b)
scalar   AIC7=log(_result(4)/_result(1))+(colsof(b7)/_result(1))*2
scalar   SIC7=log(_result(4)/_result(1))+(colsof(b7)/_result(1))*log(_result(1))
*
* Model 1.8: Drop LD.gas, LD.price, LD.income
regress  gas  L.gas          price  D.price            income  D.income
matrix   b8=get(_b)
scalar   AIC8=log(_result(4)/_result(1))+(colsof(b8)/_result(1))*2
scalar   SIC8=log(_result(4)/_result(1))+(colsof(b8)/_result(1))*log(_result(1))
*
* Model 1.9: Drop LD.gas, LD.price, D.income, LD.income
regress  gas  L.gas          price  D.price            income 
matrix   b9=get(_b)
scalar   AIC9=log(_result(4)/_result(1))+(colsof(b9)/_result(1))*2
scalar   SIC9=log(_result(4)/_result(1))+(colsof(b9)/_result(1))*log(_result(1))
*
* Model 1.10: Drop LD.gas, D.price, LD.price, LD.income 
regress  gas  L.gas          price                     income  D.income
matrix   b10=get(_b)
scalar   AIC10=log(_result(4)/_result(1))+(colsof(b10)/_result(1))*2
scalar   SIC10=log(_result(4)/_result(1))+(colsof(b10)/_result(1))*log(_result(1))
*
* Model 1.11: Drop LD.gas, D.price, LD.price, D.income, LD.income 
regress  gas  L.gas          price                     income 
matrix   b11=get(_b)
scalar   AIC11=log(_result(4)/_result(1))+(colsof(b11)/_result(1))*2
scalar   SIC11=log(_result(4)/_result(1))+(colsof(b11)/_result(1))*log(_result(1))
*
* Model 1.12: Drop all lags and differences
regress  gas                 price                     income 
matrix   b12=get(_b)
scalar   AIC12=log(_result(4)/_result(1))+(colsof(b12)/_result(1))*2
scalar   SIC12=log(_result(4)/_result(1))+(colsof(b12)/_result(1))*log(_result(1))
*
* List all calculated AICs and SICs
scalar list
clear
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