Subject index - Stata
Subject index - Stata
Subject index - Stata
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84 <strong>Subject</strong> <strong>index</strong><br />
robust, see gsem option vce(), see sem option vce()<br />
robust command, [P] robust<br />
robust regression, [R] regress, [R] rreg, also see robust,<br />
Huber/White/sandwich estimator of variance<br />
robust standard errors, [XT] Glossary<br />
robust test for equality of variance, [R] sdtest<br />
robust, Abadie–Imbens standard errors, [TE] teffects<br />
nnmatch, [TE] teffects psmatch<br />
robust, Huber/White/sandwich estimator of variance,<br />
[P] robust, [R] vce option, [SVY] variance<br />
estimation, [XT] vce options<br />
alternative-specific<br />
conditional logit model, [R] asclogit<br />
multinomial probit regression, [R] asmprobit<br />
rank-ordered probit regression, [R] asroprobit<br />
ARCH, [TS] arch<br />
ARFIMA, [TS] arfima<br />
ARIMA and ARMAX, [TS] arima<br />
competing-risks regression, [ST] stcrreg<br />
complementary log-log regression, [R] cloglog<br />
Cox proportional hazards model, [ST] stcox<br />
dynamic-factor model, [TS] dfactor<br />
fixed-effects models,<br />
linear, [XT] xtreg<br />
Poisson, [XT] xtpoisson<br />
GARCH, [TS] arch<br />
generalized linear models, [R] glm<br />
for binomial family, [R] binreg<br />
generalized method of moments, [R] gmm,<br />
[R] ivpoisson<br />
heckman selection model, [R] heckman<br />
instrumental-variables regression, [R] ivregress<br />
interval regression, [R] intreg<br />
linear dynamic panel-data estimation, [XT] xtabond,<br />
[XT] xtdpd, [XT] xtdpdsys<br />
linear regression, [R] regress<br />
constrained, [R] cnsreg<br />
truncated, [R] truncreg<br />
with dummy-variable set, [R] areg<br />
logistic regression, [R] logistic, [R] logit, also see<br />
logit regression subentry<br />
conditional, [R] clogit<br />
multinomial, [R] mlogit<br />
ordered, [R] ologit<br />
rank-ordered, [R] rologit<br />
skewed, [R] scobit<br />
stereotype, [R] slogit<br />
logit regression, [R] logistic, [R] logit, also see<br />
logistic regression subentry<br />
for grouped data, [R] glogit<br />
nested, [R] nlogit<br />
maximum likelihood estimation, [R] ml, [R] mlexp<br />
multilevel mixed-effects model, [ME] mecloglog,<br />
[ME] meglm, [ME] melogit, [ME] menbreg,<br />
[ME] meologit, [ME] meoprobit,<br />
[ME] mepoisson, [ME] meprobit, [ME] mixed<br />
robust, Huber/White/sandwich estimator of variance,<br />
continued<br />
multinomial<br />
logistic regression, [R] mlogit<br />
probit regression, [R] mprobit<br />
negative binomial regression, [R] nbreg<br />
truncated, [R] tnbreg<br />
zero-inflated, [R] zinb<br />
Newey–West regression, [TS] newey<br />
nonlinear<br />
least-squares estimation, [R] nl<br />
systems of equations, [R] nlsur<br />
parametric survival models, [ST] streg<br />
Poisson regression, [R] poisson<br />
treatment effect, [TE] etpoisson<br />
truncated, [R] tpoisson<br />
with endogenous regressors, [R] ivpoisson<br />
zero-inflated, [R] zip<br />
population-averaged models, [XT] xtgee<br />
complementary log-log, [XT] xtcloglog<br />
logit, [XT] xtlogit<br />
negative binomial, [XT] xtnbreg<br />
Poisson, [XT] xtpoisson<br />
probit, [XT] xtprobit<br />
Prais–Winsten and Cochrane–Orcutt regression,<br />
[TS] prais<br />
probit regression, [R] probit<br />
bivariate, [R] biprobit<br />
for grouped data, [R] glogit<br />
heteroskedastic, [R] hetprobit<br />
multinomial, [R] mprobit<br />
ordered, [R] heckoprobit, [R] oprobit<br />
with endogenous regressors, [R] ivprobit<br />
with sample selection, [R] heckprobit<br />
quantile regression, [R] qreg<br />
random-effects model<br />
complementary log-log, [XT] xtcloglog<br />
linear, [XT] xtreg<br />
logistic, [XT] xtlogit, [XT] xtologit<br />
Poisson, [XT] xtpoisson<br />
probit, [XT] xtoprobit, [XT] xtprobit<br />
state-space model, [TS] sspace<br />
structural equation modeling, [SEM] intro 8,<br />
[SEM] sem option method( )<br />
summary statistics,<br />
mean, [R] mean<br />
proportion, [R] proportion<br />
ratio, [R] ratio<br />
total, [R] total<br />
tobit model, [R] tobit<br />
with endogenous regressors, [R] ivtobit<br />
treatment effect, [TE] etpoisson, [TE] etregress,<br />
[TE] teffects aipw, [TE] teffects ipw,<br />
[TE] teffects ipwra, [TE] teffects ra