sysuse auto,clear

quireg price weight len

mat b = e(b)
mat se_b = cholesky(diag(vecdiag(e(V))))  

mat b0 = diag(b)
mat inv_se_b = inv(se_b)
mat t  = hadamard(b0, inv_se_b)   
mat t = vecdiag(t)'
svmat t, names(tvalue)  

list tvalue in 1/3 

* ----------Results------------------

. sysuse auto,clear
(1978 Automobile Data)

.
. reg price weight len

      Source |       SS       df       MS              Number of obs =      74
-------------+------------------------------           F(  2,    71) =   18.91
       Model |   220725280     2   110362640           Prob > F      =  0.0000
    Residual |   414340116    71  5835776.28           R-squared     =  0.3476
-------------+------------------------------           Adj R-squared =  0.3292
       Total |   635065396    73  8699525.97           Root MSE      =  2415.7

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      weight |   4.699065   1.122339     4.19   0.000     2.461184    6.936946
      length |  -97.96031    39.1746    -2.50   0.015    -176.0722   -19.84838
       _cons |   10386.54   4308.159     2.41   0.019     1796.316    18976.76
------------------------------------------------------------------------------

.
. mat b = e(b)

. mat se_b = cholesky(diag(vecdiag(e(V))))  

.
. mat b0 = diag(b)

. mat inv_se_b = inv(se_b)

. mat t  = hadamard(b0, inv_se_b)   

. mat t = vecdiag(t)'

. svmat t, names(tvalue)  

.
. list tvalue in 1/3 

     +-----------+
     |   tvalue1 |
     |-----------|
  1. |  4.186849 |
  2. | -2.500608 |
  3. |    2.4109 |
     +-----------+

[此贴子已经被作者于2007-11-6 8:44:45编辑过]