Stata Quick Reference and Index

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14 [MV] scoreplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Score and loading plots [MV] screeplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scree plot [R] tetrachoric . . . . . . . . . . . . . . . . . . . . . . . . . . Tetrachoric correlations for binary variables Generalized linear models [U] Chapter 20 . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimation and postestimation commands [U] Section 26.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Generalized linear models [R] binreg . . . . . . . . . . . . . . Generalized linear models: Extensions to the binomial family [R] glm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Generalized linear models [XT] xtgee . . . . . . . . . . . . . . . . . . Fit population-averaged panel-data models by using GEE Indicator and categorical variables [U] Section 11.4.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Factor variables [U] Chapter 25 . . . . . . . . . . . . . . . . . . . Working with categorical data and factor variables [R] fvset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Declare factor-variable settings Linear regression and related [U] Chapter 20 . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimation and postestimation commands [U] Chapter 26 . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of Stata estimation commands [R] areg . . . . . . . . . . . . . . . . . . . . . . . . . Linear regression with a large dummy-variable set [R] cnsreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constrained linear regression [R] constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Define and list constraints [R] eivreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Errors-in-variables regression [I] estimation commands . . . . . . . . . . . . . . . . . . Quick reference for estimation commands [R] fracpoly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fractional polynomial regression [R] frontier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stochastic frontier models [R] glm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Generalized linear models [R] gmm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Generalized method of moments estimation [R] heckman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heckman selection model [R] intreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interval regression [R] ivregress . . . . . . . . . . . . . . . . . . . . . . Single-equation instrumental-variables regression [R] ivtobit . . . . . . . . . . . . . . . . . . . . . . Tobit model with continuous endogenous regressors [R] logit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logistic regression, reporting coefficients [R] lpoly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kernel-weighted local polynomial smoothing [R] mfp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multivariable fractional polynomial models [R] mvreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multivariate regression [R] nbreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Negative binomial regression [R] nestreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nested model statistics [TS] newey . . . . . . . . . . . . . . . . . . . . . . . . . . . Regression with Newey–West standard errors [R] nl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nonlinear least-squares estimation [R] nlsur . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimation of nonlinear systems of equations [R] orthog . . . . . . . . . . . . . . Orthogonalize variables and compute orthogonal polynomials [R] poisson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Poisson regression [TS] prais . . . . . . . . . . . . . . . . . . . . . . . . . . Prais–Winsten and Cochrane–Orcutt regression [R] qreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quantile regression [R] reg3 . . . . . . . . . . . . . . . . Three-stage estimation for systems of simultaneous equations [R] regress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Linear regression [R] rocfit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fit ROC models [R] rreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Robust regression [ST] stcox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cox proportional hazards model [ST] stcrreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Competing-risks regression
15 [R] stepwise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stepwise estimation [ST] streg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Parametric survival models [R] sureg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zellner’s seemingly unrelated regression [R] tobit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tobit regression [R] treatreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Treatment-effects model [R] truncreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Truncated regression [R] vwls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Variance-weighted least squares [XT] xtabond . . . . . . . . . . . . . . . . . . . . Arellano–Bond linear dynamic panel-data estimation [XT] xtcloglog . . . . . . . . . . . . . . . . Random-effects and population-averaged cloglog models [XT] xtdpd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Linear dynamic panel-data estimation [XT] xtdpdsys . . . . . . Arellano–Bover/Blundell–Bond linear dynamic panel-data estimation [XT] xtfrontier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stochastic frontier models for panel data [XT] xtgee . . . . . . . . . . . . . . . . . . Fit population-averaged panel-data models by using GEE [XT] xtgls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fit panel-data models by using GLS [XT] xthtaylor . . . . . . . . . . . . . . . . Hausman–Taylor estimator for error-components models [XT] xtintreg . . . . . . . . . . . . . . . . . . . . . . . . . Random-effects interval-data regression models [XT] xtivreg . . . . . Instrumental variables and two-stage least squares for panel-data models [XT] xtlogit . . . . . . . . . Fixed-effects, random-effects, and population-averaged logit models [XT] xtmelogit . . . . . . . . . . . . . . . . . . . . . . . . . . . Multilevel mixed-effects logistic regression [XT] xtmepoisson . . . . . . . . . . . . . . . . . . . . . . . . Multilevel mixed-effects Poisson regression [XT] xtmixed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multilevel mixed-effects linear regression [XT] xtnbreg Fixed-effects, random-effects, & population-averaged negative binomial models [XT] xtpcse . . . . . . . . . . . . . . . . . . . . Linear regression with panel-corrected standard errors [XT] xtpoisson . . . . Fixed-effects, random-effects, and population-averaged Poisson models [XT] xtprobit . . . . . . . . . . . . . . . . . . Random-effects and population-averaged probit models [XT] xtrc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Random-coefficients model [XT] xtreg . . Fixed-, between-, and random-effects, and population-averaged linear models [XT] xtregar . . . . . . . . . Fixed- and random-effects linear models with an AR(1) disturbance [XT] xttobit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Random-effects tobit models [R] zinb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zero-inflated negative binomial regression [R] zip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zero-inflated Poisson regression [R] ztnb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zero-truncated negative binomial regression [R] ztp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zero-truncated Poisson regression Logistic and probit regression [U] Chapter 20 . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimation and postestimation commands [U] Chapter 26 . . . . . . . . . . . . . . . . . . . . . . . . . . . Overview of Stata estimation commands [R] asclogit . . . . . . . . . . Alternative-specific conditional logit (McFadden’s choice) model [R] asmprobit . . . . . . . . . . . . . . . . . . . . . Alternative-specific multinomial probit regression [R] asroprobit . . . . . . . . . . . . . . . . . . . . Alternative-specific rank-ordered probit regression [R] biprobit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bivariate probit regression [R] clogit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conditional (fixed-effects) logistic regression [R] cloglog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Complementary log-log regression [R] exlogistic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exact logistic regression [R] glogit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logit and probit regression for grouped data [R] heckprob . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Probit model with sample selection [R] hetprob . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heteroskedastic probit model [R] ivprobit . . . . . . . . . . . . . . . . . . . . Probit model with continuous endogenous regressors [R] logistic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logistic regression, reporting odds ratios [R] logit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logistic regression, reporting coefficients [R] mlogit . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multinomial (polytomous) logistic regression
Page 1 and 2: STATA QUICK REFERENCE AND INDEX REL
Page 3: i Table of contents Combined subjec
Page 6 and 7: 2 Inputting and saving data [GS] Ch
Page 8 and 9: 4 [D] changeeol . . . . . . . . . .
Page 10 and 11: 6 [R] estimates title . . . . . . .
Page 12 and 13: 8 [G] graph twoway lowess . . . . .
Page 14 and 15: 10 Graph schemes [G] schemes intro
Page 16 and 17: 12 [MV] clustermat . . . . . . . .
Page 20 and 21: 16 [R] mprobit . . . . . . . . . .
Page 22 and 23: 18 Nonparametric statistics [R] kde
Page 24 and 25: 20 [R] predict . . . . . . . . . .
Page 26 and 27: 22 [TS] tsreport . . . . . . . . .
Page 28 and 29: 24 [P] fvexpand . . . . . . . . . .
Page 30 and 31: 26 Mata [M] Mata Reference Manual .
Page 32 and 33: Title estimation commands — Quick
Page 34 and 35: 30 estimation commands — Quick re
Page 40 and 41: Title format — Quick reference fo
Page 42 and 43: Title missing values — Quick refe
Page 44 and 45: 40 postestimation commands — Quic
Page 52 and 53: Title prefix commands — Quick ref
Page 54 and 55: Title reading data — Quick refere
Page 57 and 58: Title Acronym glossary 2SIV 2SLS 3S
Page 59 and 60: Acronym glossary 55 IC ICD-9 IIA i.
Page 61 and 62: Acronym glossary 57 QDA QML rc RCT
Page 63 and 64: Vignettes index 59 Vignettes index
Page 65 and 66: Author index 61 Author index A Aale
Page 67 and 68: Author index 63 Blackburne III, E.
Page 69 and 70:
Author index 65 Conover, W. J., [R]
Page 71 and 72:
Author index 67 Feinleib, M., [XT]
Page 73 and 74:
Author index 69 Green, P. E., [MV]
Page 75 and 76:
Author index 71 Hipel, K. W., [TS]
Page 77 and 78:
Author index 73 Klein, J. P., [ST]
Page 79 and 80:
Author index 75 Lu, J. Y., [TS] pra
Page 81 and 82:
Author index 77 Muirhead, R. J., [M
Page 83 and 84:
Author index 79 Ponce de Leon, A.,
Page 85 and 86:
Author index 81 Schonlau, M., [R] g
Page 87 and 88:
Author index 83 ter Bogt, T., [MV]
Page 89 and 90:
Author index 85 Wichern, D. W., con
Page 91 and 92:
Subject index 87 Subject index ! (n
Page 93 and 94:
Subject index 89 args command, [P]
Page 95 and 96:
Subject index 91 bias corrected and
Page 97 and 98:
Subject index 93 c(minbyte) c-class
Page 99 and 100:
Subject index 95 choice models, con
Page 101 and 102:
Subject index 97 cointegration, con
Page 103 and 104:
Subject index 99 constrained estima
Page 105 and 106:
Subject index 101 cumsp command, [T
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Subject index 103 describe, continu
Page 109 and 110:
Subject index 105 DLL, [P] plugin D
Page 111 and 112:
Subject index 107 end-of-line chara
Page 113 and 114:
Subject index 109 estat, continued
Page 115 and 116:
Subject index 111 FEVD, [TS] Glossa
Page 117 and 118:
Subject index 113 fsave, window sub
Page 119 and 120:
Subject index 115 graph, continued
Page 121 and 122:
Subject index 117 Harrell’s C, [S
Page 123 and 124:
Subject index 119 Im-Pesaran-Shin t
Page 125 and 126:
Subject index 121 invfft() function
Page 127 and 128:
Subject index 123 label values, [P]
Page 129 and 130:
Subject index 125 loading, [MV] Glo
Page 131 and 132:
Subject index 127 markers, [G] mark
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Subject index 129 mband, graph twow
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Subject index 131 missing data, [MI
Page 137 and 138:
Subject index 133 mvdecode command,
Page 139 and 140:
Subject index 135 number to string
Page 141 and 142:
Subject index 137 parameter trace f
Page 143 and 144:
Subject index 139 polytomous logist
Page 145 and 146:
Subject index 141 proportions, [R]
Page 147 and 148:
Subject index 143 rc built-in varia
Page 149 and 150:
Subject index 145 return, [M-2] ret
Page 151 and 152:
Subject index 147 RVI, see relative
Page 153 and 154:
Subject index 149 set, continued fa
Page 155 and 156:
Subject index 151 Sneath and Sokel
Page 157 and 158:
Subject index 153 Stata, continued
Page 159 and 160:
Subject index 155 stset command for
Page 161 and 162:
Subject index 157 svy: truncreg com
Page 163 and 164:
Subject index 159 tests, continued
Page 165 and 166:
Subject index 161 truncating real n
Page 167 and 168:
Subject index 163 variables, contin
Page 169 and 170:
Subject index 165 weights, continue
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