Title stata.com regress — Linear regression

1 Linear regressionSyntaxMenuDescriptionOptionsRe marks and examplesStored resultsMethods and formulasAcknowledgmentsReferencesAlso seeSyntaxregressdepvar[indepvars] [if] [in] [weight] [,options]optionsDescriptionModelnoconst antsuppress constant termhasconshas user-supplied constanttssconscompute total sum of squares with constant; seldom usedSE/Robustvce(vcetype)vcetypemay beols,robust,clusterclustvar,bootstrap,j ackknife,hc2, orhc3 Reportinglevel(#)set confidence level; default islevel(95)betareport standardized beta coefficientseform(string)report exponentiated coefficients and label asstringdepname(varname)substitute dependent variable name.

2 Programmer s optiondisplayoptionscontrol column formats, row spacing, line width, display of omittedvariables and base and empty cells, and factor-variable labelingnoheadersuppress output headernotablesuppress coefficient tableplusmake table extendablemse1force mean squared error to1coeflegenddisplay legend instead of statisticsindepvarsmay contain factor variables; see[U] Factor contain time-series operators; see[U] Time-series ,by,fp,jackknife,mfp,mi estimate,nestreg,rolling,statsby,stepwis e, andsvyare allowed;see[U] Prefix (bootstrap)andvce(jackknife)are not allowed with themi estimateprefix; see [MI]mi are not allowed with thebootstrapprefix; see [R] are not allowed with thejackknifeprefix; see [R] ,tsscons,vce(),beta,noheader,notable,plu s,depname(),mse1, and weights are not allowed withthesvyprefix; see [SVY] ,fweights,iweights, andpweights are allowed.

3 See[U] ,notable,plus,mse1, andcoeflegenddo not appear in the dialog [U] 20 Estimation and postestimation commandsfor more capabilities of estimation regress Linear regressionMenuStatistics> Linear models and related> Linear regressionDescriptionregressfits a model ofdepvaronindepvarsusing Linear is a short list of other regression commands that may be of interest. Seehelp estimationcommandsfor a complete [R]aregan easier way to fit regressions with many dummy variablesarch[TS]archregression models with ARCH errorsarima[TS]arimaARIMA modelsboxcox[R]boxcoxBox Cox regression modelscnsreg[R]cnsregconstrained Linear regressioneivreg[R]eivregerrors-in-varia bles regressionetregress[TE]etregressLinear regression with endogenous treatment effectsfrontier[R]frontierstochastic frontier modelsgmm[R]gmmgeneralized method of moments estimationheckman[R]

4 HeckmanHeckman selection modelintreg[R]intreginterval regressionivregress[R]ivregresssingle-eq uation instrumental-variables regressionivtobit[R]ivtobittobit regression with endogenous variablesnewey[TS]neweyregression with Newey West standard errorsnl[R]nlnonlinear least-squares estimationnlsur[R]nlsurestimation of nonlinear systems of equationsqreg[R]qregquantile (including median) regressionreg3[R]reg3three-stage least-squares (3 SLS) regressionrreg[R]rrega type of robust regressiongsem[SEM]intro 5generalized structural equation modelssem[SEM]intro 5linear structural equation modelssureg[R]suregseemingly unrelated regressiontobit[R]tobittobit regressiontruncreg[R]truncregtruncated regressionxtabond[XT]xtabondArellano Bond Linear dynamic panel-data estimationxtdpd[XT]xtdpdlinear dynamic panel-data estimationxtfrontier[XT]xtfrontierpanel- data stochastic frontier modelsxtgls[XT]xtglspanel-data GLS modelsxthtaylor[XT]

5 XthtaylorHausman Taylor estimator for error-components modelsxtintreg[XT]xtintregpanel-data interval regression modelsxtivreg[XT]xtivregpanel-data instrumental-variables (2 SLS) regressionxtpcse[XT]xtpcselinear regression with panel-corrected standard errorsxtreg[XT]xtregfixed- and random-effects Linear modelsxtregar[XT]xtregarfixed- and random-effects Linear models with an AR(1) disturbancexttobit[XT]xttobitpanel-data tobit modelsregress Linear regression 3 Options Model noconstant; see [R]estimation that a user-defined constant or its equivalent is specified among the independentvariables inindepvars.

6 Some caution is recommended when specifying this option, as resultingestimates may not be as accurate as they otherwise would be. Use of this option requires sweeping the constant last, so the moment matrix must be accumulated in absolute rather than deviation option may be safely specified when the means of the dependent and independent variablesare all reasonable and there is not much collinearity between the independent variables. The bestprocedure is to viewhasconsas a reporting option estimate with and withouthasconsandverify that the coefficients and standard errors of the variables not affected by the identity of theconstant are 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.

7 This is a rarely used option that has aneffect only when specified withnoconstant. It affects the total sum of squares and all resultsderived from the total sum of squares. SE/Robust vce(vcetype)specifies the type of standard error reported, which includes types that are derivedfrom asymptotic theory (ols), that are robust to some kinds of misspecification (robust), thatallow for intragroup correlation (clusterclustvar), and that use bootstrap or jackknife methods(bootstrap,jackknife).

8 See [R] (ols), the default, uses the standard variance estimator for ordinary least-squares allows the following:vce(hc2)andvce(hc3)specify an alternative bias correction for the robust variance (hc2)andvce(hc3)may not be specified withsvyprefix. In the unclustered case,vce(robust)uses 2j={n/(n k)}u2jas an estimate of the variance of thejth observation,whereujis the calculated residual andn/(n k)is included to improve the overall estimate ssmall-sample (hc2)instead usesu2j/(1 hjj)as the observation s variance estimate, wherehjjis thediagonal element of the hat (projection) matrix.

9 This estimate is unbiased if the model reallyis (hc2)tends to produce slightly more conservative confidence (hc3)usesu2j/(1 hjj)2as suggested by Davidson and MacKinnon (1993), who reportthat this method tends to produce better results when the model really is (hc3)produces confidence intervals that tend to be even more Davidson and MacKinnon (1993, 554 556) and Angrist and Pischke (2009, 294 308) formore discussion on these two bias corrections. Reporting level(#); see [R]estimation that standardized beta coefficients be reported instead of confidence intervals.

10 The betacoefficients are the regression coefficients obtained by first standardizing all variables to have amean of 0 and a standard deviation of not be specified withvce(clusterclustvar)or regress Linear regressioneform(string)is used only in programs and ado-files that useregressto fit models other thanlinear ()specifies that the coefficient table be displayed in exponentiated formas defined in [R]maximizeand thatstringbe used to label the exponentiated coefficients in (varname)is used only in programs and ado-files that useregressto fit models other thanlinear ()may be specified only at estimation recorded asthe identity of the dependent variable, even though the estimates are calculated usingdepvar.