SAMPLE 1 1974
READ (DMBP.txt) Y DAYDUM

* Covariance matrix - Information matrix  
HET Y / GARCH=1 PRESAMP COEF=BETA STDERR=IM COV=AINV GMATRIX=G 

* Compute robust standard errors (see Weiss and Bollerslev-Wooldridge)
MATRIX V=AINV*(G'G)*AINV
MATRIX BW=SQRT(DIAG(V))

* Covariance matrix - outer product of gradient (OP)
?HET Y / GARCH=1 PRESAMP OPGCOV STDERR=OP

* Covariance matrix - Hessian estimation by numerical derivatives
?HET Y / GARCH=1 PRESAMP NUMCOV STDERR=HNUM

* List the coefficients and standard errors
SAMPLE 1 4
PRINT BETA HNUM OP IM BW 

* Compare with the benchmark results in McCullough and Renfro 
* Benchmark coefficients 
READ BMARK / BYVAR
 -0.00619041  0.0107613  0.153134  0.805974
* Calculate the log relative error -- the number of digits of accuracy
GENR LRE=-LOG(ABS(BETA-BMARK)/ABS(BMARK))/2.3026
PRINT BMARK BETA LRE
* Benchmark standard error estimates
READ BH BOP BIM BBW 
 0.00846212  0.00843359  0.00837628  0.00873092
 0.00285271  0.00132298  0.00192881  0.00312364
 0.0265228   0.0139737   0.0194012   0.0273219
 0.0335527   0.0165604   0.0218399   0.0301509
GENR LRE1=-LOG(ABS(HNUM-BH)/ABS(BH))/2.3026  
GENR LRE2=-LOG(ABS(OP-BOP)/ABS(BOP))/2.3026  
GENR LRE3=-LOG(ABS(IM-BIM)/ABS(BIM))/2.3026  
GENR LRE4=-LOG(ABS(BW-BBW)/ABS(BBW))/2.3026  
FORMAT(4F12.2)
PRINT LRE1 LRE2 LRE3 LRE4 / FORMAT
STOP