Seemingly unrelated regressions model with normaldisturbances

 <<< Model specification read from file priorsur1                               

 <<< Data read from file datasur1                                

 Number of variables read from file:     5
     Number of observations in file:   200
       Observations used for sample:     1 -   200

 Col     Minimum     Maximum        Mean  Stan. dev.       First        Last
   1 -2.4856E+00  2.7962E+00  1.4391E-01  9.4982E-01 -1.2569E-01 -9.3567E-01
   2 -2.1539E+00  2.6108E+00  5.5842E-02  9.6550E-01 -4.9249E-01 -3.2180E-01
   3 -2.0934E+00  3.0342E+00 -3.0754E-02  9.2517E-01 -5.8469E-01 -1.1301E+00
   4 -6.5041E+00  5.7020E+00  2.9202E-02  2.3412E+00 -2.1061E+00 -2.0461E+00
   5 -7.9876E+00  8.6969E+00  4.7811E-02  3.1016E+00 -1.0617E+00  1.1187E+00

       First observation (t1):    1
        Last observation (t2):  200

 Equation    Dep var    Regressors
    1           4       1  2  3
    2           5       1  2  3

 Gaussian prior for coefficients
 Parameter      Prior mean      Prior s.d.     Prior prec.   Initial value
         1    0.000000E+00    3.000000E+00    1.111111E-01   -4.300032E+00
         2    0.000000E+00    3.000000E+00    1.111111E-01   -1.440809E-01
         3    0.000000E+00    3.000000E+00    1.111111E-01   -1.467723E+00
         4    0.000000E+00    3.000000E+00    1.111111E-01    8.302309E-01
         5    0.000000E+00    3.000000E+00    1.111111E-01    6.278369E+00
         6    0.000000E+00    3.000000E+00    1.111111E-01   -5.031770E+00

  Prior correlation matrix

         1      2      3      4      5      6
    1  1.000
    2  0.000  1.000
    3  0.000  0.000  1.000
    4  0.000  0.000  0.000  1.000
    5  0.000  0.000  0.000  0.000  1.000
    6  0.000  0.000  0.000  0.000  0.000  1.000

  Normalized prior precision matrix

         1      2      3      4      5      6
    1  1.000
    2  0.000  1.000
    3  0.000  0.000  1.000
    4  0.000  0.000  0.000  1.000
    5  0.000  0.000  0.000  0.000  1.000
    6  0.000  0.000  0.000  0.000  0.000  1.000

 Wishart prior distribution for precision matrix
 with   5.0000000E+00 degrees of freedom

 Matrix parameter S0: H~W[S0^-1, ANU0]

              1              2
   1   2.000000E-01
   2   0.000000E+00   2.000000E-01

 Inverse of matrix parameter S0

              1              2
   1   5.000000E+00
   2   0.000000E+00   5.000000E+00

 Initial draw of H

              1              2
   1   1.912844E+01
   2  -1.007415E+01   1.498468E+01

 Inverse of initial draw of H

              1              2
   1   8.093485E-02
   2   5.441224E-02   1.033160E-01

 Organization of simulation vector
             Covariate coefficient vector (Beta):    1
                Antithetic of coefficient vector:    7
 Disturbance precision matrix H (lower triangle):   13

  Markov chain Monte Carlo posterior simulation
    Parameters written in each record:        15
                           Iterations:      1000
             Skips between iterations:         0

 >>> Posterior simulation matrix written to file simsur1                                 

  Description:
  This is a shakedown run for sur1, using all variables in mvr set1.         

   Iterations completed:        1000
               CPU time:      0.0000
  CPU time per iteraton:      0.0000