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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

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