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 theolsOut
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 -------------------------------------