The Stata command **sureg** runs a seemingly unrelated regression (SUR). That is a regression in which two (or more)
unrelated outcome variables are predicted by sets of predictor variables. These predictor
variables may or may not be the same for the two outcomes. If the set of predictor variables
is identical across the two outcomes, the results from sureg will be identical to those from OLS. In
other cases (i.e. non-identical prediction equations), SUR produces more efficient estimates than OLS.
It does this by weighting the estimates by the covariance of the residuals from the individual regressions.
See Greene (2005 p 340-351) for additional information on seemingly unrelated
regression. Below we show how to replicate the results of Stata’s sureg command.

We will fit the following model:

read = b_0 + b_1*math + b_2*write + b_3*socst + e_r science = g_0 + g_1*female + g_2*math + e_s

The coefficients **b_0**, **b_1**, **b_2**, and **b_3**, are the intercept and regression coefficients for
**read**, and **e_r** is the error term for **read**. The coefficients **g_0**, **g_1**, and **g_2** are
the regression coefficients for **science**, and **e_s** is the error term for **science**.

The matrix form of the equation for these coefficients is:

Where X is a matrix of predictors, Y is a vector of outcomes, and V is:

that is the Kronecker product of **S** and **I**. Where **S** is the variance
covariance matrix of OLS residuals and **I** is an identity matrix of size n
equal to the number of cases in the analysis.

Below we open the dataset and then run the above model using the **sureg**
command.

use https://stats.idre.ucla.edu/stat/data/hsb2, clear sureg (read math write socst) (science female math)Seemingly unrelated regression ---------------------------------------------------------------------- Equation Obs Parms RMSE "R-sq" chi2 P ---------------------------------------------------------------------- read 200 3 6.884452 0.5469 234.17 0.0000 science 200 2 7.591249 0.4092 137.41 0.0000 ---------------------------------------------------------------------- ------------------------------------------------------------------------------ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- read | math | .4820974 .0676596 7.13 0.000 .3494869 .6147079 write | .1112919 .0692286 1.61 0.108 -.0243936 .2469775 socst | .2775624 .0570989 4.86 0.000 .1656506 .3894741 _cons | 6.430895 3.096425 2.08 0.038 .3620135 12.49978 -------------+---------------------------------------------------------------- science | female | -1.687415 1.045627 -1.61 0.107 -3.736806 .3619758 math | .663942 .0574353 11.56 0.000 .5513709 .7765132 _cons | 17.81641 3.139002 5.68 0.000 11.66408 23.96874 ------------------------------------------------------------------------------

We will use the **regress**
command to predict **read** using **write**, **math, **and **socst**. After we run the
regression we use predict to create a new variable **r_resid** which contains
the residual for each case. Further below we repeat the last two steps for the
model predicting **science**.

regress read write math socst predict r_read, resid regress science female math predict r_sci, resid

We use the **cor** (correlate) command with the **cov** option
to obtain the covariance matrix for the residuals from the above regressions. We
store this matrix as **s**, a 2 by 2 symmetric matrix. Then we create another matrix **i**, which is
an identity matrix with the number of rows and columns equal to the number of
cases in the analysis, i.e., **i** is a 200 by 200 identity matrix. Finally, the matrix **v** is the Kronecker
product of **s** and **i** resulting in a 400 by 400 matrix.

cor r_read r_sci, cov mat s = r(C) mat i = I(200) mat v = s#i gen cons = 1

Below is an example of what the **X** matrix should look like when we are done.
The first two lines of the matrix shown below are the lines for the first equation (with
additional cases omitted), the second set of lines shows the lines for the
second equation. Note
that the math scores are the same, since the same two hypothetical cases are shown.

math write socst cons female math cons 53 45 62 1 0 0 0 46 53 58 1 0 0 0 ... 0 0 0 0 1 53 1 0 0 0 0 0 46 1 ...

The code below takes the values of the predictor variables for the first equation
(i.e., **math write socst cons**) from the dataset and places them in a matrix,
**x_read**. In the
second line of code below a matrix of zeros produced by the function **J(200,3,0)**
with 200 rows (n=200) and 3 columns (for three variables in the second equation) is placed
to the right of the values from the dataset. A similar process takes place for the predictors
from the second equation (**x_sci**) except this time the matrix of zeros is
200 by 4, and is placed to the left of the values from the dataset. The final
line of code below stacks the matrix for the first equation (**x_read**) on top of
the matrix for the second equation (**x_sci**), creating a
single matrix **x**, with 400 rows and 7 columns.

mkmat math write socst cons, matrix(x_read) mat x_read = x_read , J(200,3,0) mkmat female math cons, matrix(x_sci) mat x_sci = J(200,4,0) , x_sci mat x = x_read \x_sci

First two vectors are created, one for each of the two dependent variables (**read**
and **science**), then the vector of read values is stacked on top of the
vector of science values to create a single vector **y** with 400 rows.

mkmat read, matrix(y_read) mkmat science, matrix(y_sci) mat y = y_read \y_sci

Finally we compute the weighted estimates, producing the vector **b** with 7 rows.
Then we can list the vector to look at our parameter estimates. Note that these
are the same as the coefficient estimates produced by **sureg**.

mat b = inv(x'*inv(v)*x)*x'*inv(v)*y mat list bb[7,1] read math .4820974 write .11129194 socst .27756235 cons 6.4308952 c1 -1.687415 c2 .66394202 c3 17.816414

## Using Mata

For the relatively small example above, we could use Stata’s matrix functions to reproduce the estimates from the sureg. However, if you wanted to do this with a larger example, you might need to use Mata. Below is the code to reproduce the same example using Stata and Mata.

use https://stats.idre.ucla.edu/stat/data/hsb2, clear sureg (read math write socst) (science female math) * run both models and get residuals reg read math write socst predict r_read, resid reg science female math predict r_sci, resid * create variable to act as constant in regressions capture gen cons = 1 * get the covariance between the residuals cor r_read r_sci, cov mata: /* calculate v the weight matrix based on the error covariances */ s = st_matrix("r(C)") i = I(200) v = s#i /* matrix for x variables for read (plus appropriate zeros) */ x_read = st_data(.,("math", "write", "socst", "cons")) x_read = x_read , J(200,3,0) /* matrix for x variables for science (prefixed by the appropriate zeros) */ x_sci = st_data(.,("female", "math", "cons")) x_sci = J(200,4,0) , x_sci /* Stack matrix for first equation on top of matrix for second equation */ x = x_read \ x_sci /* vectors for each of the y variables, stack read on top of science */ y = st_data(.,"read") \ st_data(.,"science") /* calculate the estimates and post them back to the Stata matrix b */ b = qrinv(x'*luinv(v)*x)*x'*luinv(v)*y st_matrix("b",b) endmat li b0b0[1,7] read: read: read: read: science: science: science: math write socst _cons female math _cons y1 .4820974 .11129194 .27756235 6.4308952 -1.687415 .66394202 17.816414mat li b1b1[1,7] c1 c2 c3 c4 c5 c6 c7 r1 .4820974 .11129194 .27756235 6.4308952 -1.687415 .66394202 17.816414

## References

Greene, William H. (2005). Econometric Analysis. Fifth edition. Pearson Education.

## See also

Stata FAQ: What is seemingly unrelated regression and how can I perform it in Stata?