clusterSE¶

Purpose¶

Procedure to compute the White cluster-robust standard errors.

Format¶

vce_cluster = clusterSE(x, grp, resid[, const[, verbose, var_names])¶

vce_cluster = clusterSE(dataset, formula, grp_var, resid[, const, verbose, var_names])

vce_cluster = clusterSE(dataframe, formula, grp_var, resid[, const, verbose, var_names])

Parameters:

x (NxK matrix) – independent regression variables, should not include a const.
grp (NTx1 matrix) – vector of group indicators.
resid (NTx1 matrix) –
ols residuals.

Note

if using olsmt() these are stored in the olsOut structure member resid.
dataset (string) – name of dataset.
formula (String) – formula string of the independent variables. E.g "X1 + X2", ‘X1’ and ‘X2’ are names of independent variables;
grp_var (string) – name of the group variable.
const (scalar) – Optional input, indicator variable for including a const. 1 for including a const, 0 for no const. Default = 1.
verbose (scalar) – Optional input, 1 to print results, 0 for no printing. Default = 1.
var_names (string array) – Optional input, variable names. Default = X1, X2, …, XK.
ss (Scalar) – Optional input, indicator variable for using the small sample correction. 1 to compute the small sample correction, 0 for no correction. Default = 1.

Returns:

vce_cluster (KxK matrix) – White cluster-robust variance-covariance matrix.

Examples¶

new;

// Load data using auto dataset
fname = getGAUSSHome("examples/regsmpl.dta");
data = loadd(fname);

// Control structure
struct olsmtControl o_ctl;
o_ctl = olsmtControlCreate();

// Turn on to estimate residuals
o_ctl.res = 1;

// Declare output structure
struct olsmtOut o_out;

// Run initial ols
o_out = olsmt(fname, "ln_wage ~ age + age:age + tenure", o_ctl);

This estimates the OLS regression and finds the i.i.d. standard errors:

Valid cases:                 28101      Dependent variable:             ln_wage
Missing cases:                 433      Deletion method:               Listwise
Total SS:                 6414.965      Degrees of freedom:               28097
R-squared:                   0.164      Rbar-squared:                     0.164
Residual SS:              5360.440      Std error of est:                 0.437
F(3,28097):               1842.448      Probability of F:                 0.000
Durbin-Watson:               0.906

                         Standard                 Prob   Standardized  Cor with
Variable     Estimate      Error      t-value     >|t|     Estimate    Dep Var
-------------------------------------------------------------------------------

const        0.333982    0.050441    6.621206     0.000       ---         ---
age          0.075217    0.003474   21.653863     0.000    1.054270    0.278922
age:age     -0.001085    0.000058  -18.862899     0.000   -0.916788    0.265497
tenure       0.039088    0.000774   50.479037     0.000    0.306895    0.370584

Calling clusterSE() estimates the cluster-robust standard errors:

// Find cluster-robust standard errors regression includes const
vce_cluster = clusterse(fname, "age + age:age + tenure", "idcode", o_out.resid );

The results:

Total observations:                                        28101
Number of variables:                                           4

          VARIABLE     Clustered SE
  -------------------------------------

             const         0.064192
               age        0.0045711
           age:age       7.7846e-05
            tenure        0.0014425
  -------------------------------------