In the unclustered case, vce(robust) uses (sigma-hat_j)^2=(u_j)^2 as an estimate of the variance of the jth observation, where u_j is the calculated residual and n/(n-k) is included to improve the overall estimate's small-sample properties. vce(hc2) and vce(hc3) may not be specified with the svy prefix. Vce(hc2) and vce(hc3) specify an alternative bias correction for the robust variance calculation. Vce(ols), the default, uses the standard variance estimator for ordinary least-squares regression. Vce( vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory ( ols), that are robust to some kinds of misspecification ( robust), that allow for intragroup correlation ( cluster clustvar), and that use bootstrap or jackknife methods ( bootstrap, jackknife) see vce_option. It affects the total sum of squares and all results derived from the total sum of squares. This is a rarely used option that has an effect only when specified with noconstant. Tsscons forces the total sum of squares to be computed as though the model has a constant, that is, as deviations from the mean of the dependent variable. The best procedure is to view hascons as a reporting option - estimate with and without hascons and verify that the coefficients and standard errors of the variables not affected by the identity of the constant are unchanged. This option may be safely specified when the means of the dependent and independent variables are all reasonable and there is not much collinearity between the independent variables. Use of this option requires "sweeping" the constant last, so the moment matrix must be accumulated in absolute rather than deviation form. Some caution is recommended when specifying this option, as resulting estimates may not be as accurate as they otherwise would be. Hascons indicates that a user-defined constant or its equivalent is specified among the independent variables in indepvars. See estimation commands for a list of other regression commands that may be of interest. Regress fits a model of depvar on indepvars using linear regression. Statistics > Linear models and related > Linear regression Description See regress postestimation for features available after estimation. Noheader, notable, plus, mse1, and coeflegend do not appear in the dialog box. Hascons, tsscons, vce( ), beta, noheader, notable, plus, depname( ), mse1, and weights are not allowed with the svy prefix.Īweights, fweights, iweights, and pweights are allowed see weight. Weights are not allowed with the bootstrap prefix.Īweights are not allowed with the jackknife prefix. Vce(bootstrap) and vce(jackknife) are not allowed with the mi estimate prefix. Indepvars may contain factor variables see fvvarlist.ĭepvar and indepvars may contain time-series operators see tsvarlist.īootstrap, by, fp, jackknife, mfp, mi estimate, nestreg, rolling, statsby, stepwise, and svy are allowed see prefix. Substitute dependent variable name programmer's optionĬontrol columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling Report exponentiated coefficients and label as string Set confidence level default is level(95) Vcetype may be ols, robust, cluster clustvar, bootstrap, jackknife, hc2, or hc3 Regress depvar OptionsĬompute total sum of squares with constant seldom used regress postestimation diagnostic plots.