Stata Quick Reference and Index

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22 [TS] tsreport . . . . . . . . . . . . . . Report time-series aspects of a dataset or estimation sample [TS] tsrevar . . . . . . . . . . . . . . . . . . . . . . . . . . . . Time-series operator programming command [TS] tsset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Declare data to be time-series data [TS] var intro . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction to vector autoregressive models [TS] var svar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structural vector autoregressive models [TS] var . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vector autoregressive models [TS] varbasic . . . . . . . . . . . . . . . . . . . . . . . . . . Fit a simple VAR and graph IRFS or FEVDs [TS] vargranger . . . . . . . . . . . . . . Perform pairwise Granger causality tests after var or svar [TS] varlmar . . . . . . . . . . . . . Perform LM test for residual autocorrelation after var or svar [TS] varnorm . . . . . . . . . . . . . . . Test for normally distributed disturbances after var or svar [TS] varsoc . . . . . . . . . . . . . . . . Obtain lag-order selection statistics for VARs and VECMs [TS] varstable . . . . . . . . . . . . . . . Check the stability condition of VAR or SVAR estimates [TS] varwle . . . . . . . . . . . . . . . . . . . . . Obtain Wald lag-exclusion statistics after var or svar [TS] vec intro . . . . . . . . . . . . . . . . . . . . . . . . . Introduction to vector error-correction models [TS] vec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vector error-correction models [TS] veclmar . . . . . . . . . . . . . . . . . . . Perform LM test for residual autocorrelation after vec [TS] vecnorm . . . . . . . . . . . . . . . . . . . . Test for normally distributed disturbances after vec [TS] vecrank . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimate the cointegrating rank of a VECM [TS] vecstable . . . . . . . . . . . . . . . . . . . . . . Check the stability condition of VECM estimates [TS] xcorr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-correlogram for bivariate time series Time series, univariate [U] Section 11.4.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Time-series varlists [U] Section 13.9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Time-series operators [U] Section 26.14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Models with time-series data [TS] arch . . . . . Autoregressive conditional heteroskedasticity (ARCH) family of estimators [TS] arima . . . . . . . . . . . . . . . . . . ARIMA, ARMAX, and other dynamic regression models [TS] corrgram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tabulate and graph autocorrelations [TS] cumsp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cumulative spectral distribution [TS] dfgls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . DF-GLS unit-root test [TS] dfuller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Augmented Dickey–Fuller unit-root test [TS] haver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Load data from Haver Analytics database [TS] newey . . . . . . . . . . . . . . . . . . . . . . . . . . . Regression with Newey–West standard errors [TS] pergram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Periodogram [TS] pperron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phillips–Perron unit-root test [TS] prais . . . . . . . . . . . . . . . . . . . . . . . . . . Prais–Winsten and Cochrane–Orcutt regression [TS] rolling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rolling-window and recursive estimation [TS] time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction to time-series commands [TS] tsappend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Add observations to a time-series dataset [TS] tsfill . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fill in gaps in time variable [TS] tsline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Plot time-series data [TS] tsreport . . . . . . . . . . . . . . Report time-series aspects of a dataset or estimation sample [TS] tsrevar . . . . . . . . . . . . . . . . . . . . . . . . . . . . Time-series operator programming command [TS] tsset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Declare data to be time-series data [TS] tssmooth . . . . . . . . . . . . . . . . . . . . . . . Smooth and forecast univariate time-series data [TS] tssmooth dexponential . . . . . . . . . . . . . . . . . . . . . . . . . . . Double-exponential smoothing [TS] tssmooth exponential . . . . . . . . . . . . . . . . . . . . . . . . . . . . Single-exponential smoothing [TS] tssmooth hwinters . . . . . . . . . . . . . . . . . . . . . . . . Holt–Winters nonseasonal smoothing [TS] tssmooth ma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Moving-average filter [TS] tssmooth nl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nonlinear filter [TS] tssmooth shwinters . . . . . . . . . . . . . . . . . . . . . . . . . . . Holt–Winters seasonal smoothing
23 [TS] wntestb . . . . . . . . . . . . . . . . . . . . . . . Bartlett’s periodogram-based test for white noise [TS] wntestq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Portmanteau (Q) test for white noise [TS] xcorr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-correlogram for bivariate time series Transforms and normality tests [R] boxcox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Box–Cox regression models [R] fracpoly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fractional polynomial regression [R] ladder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ladder of powers [R] lnskew0 . . . . . . . . . . . . . . . . . . . . . . . . . Find zero-skewness log or Box–Cox transform [R] mfp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multivariable fractional polynomial models [MV] mvtest normality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multivariate normality tests [R] sktest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Skewness and kurtosis test for normality [R] swilk . . . . . . . . . . . . . . . . . . . . Shapiro–Wilk and Shapiro–Francia tests for normality Matrix commands Basics [U] Chapter 14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matrix expressions [P] matlist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Display a matrix and control its format [P] matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction to matrix commands [P] matrix define . . . . . . . . . . . . . . . . . . . . . . . . Matrix definition, operators, and functions [P] matrix utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . List, rename, and drop matrices Programming [P] ereturn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Post the estimation results [P] matrix accum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Form cross-product matrices [P] matrix rownames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Name rows and columns [P] matrix score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Score data from coefficient vectors [R] ml . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maximum likelihood estimation Other [P] makecns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Constrained estimation [P] matrix dissimilarity . . . . . . . . . . . . . . . . . Compute similarity or dissimilarity measures [P] matrix eigenvalues . . . . . . . . . . . . . . . . . . . . . . . Eigenvalues of nonsymmetric matrices [P] matrix get . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Access system matrices [P] matrix mkmat . . . . . . . . . . . . . . . . . . . . . . . . Convert variables to matrix and vice versa [P] matrix svd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Singular value decomposition [P] matrix symeigen . . . . . . . . . . . . . . Eigenvalues and eigenvectors of symmetric matrices Mata [M] Mata Reference Manual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Programming Basics [U] Chapter 18 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Programming Stata [U] Section 18.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Macros [U] Section 18.11 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ado-files [P] comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Add comments to programs
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 18 and 19: 14 [MV] scoreplot . . . . . . . . .
Page 20 and 21: 16 [R] mprobit . . . . . . . . . .
Page 22 and 23: 18 Nonparametric statistics [R] kde
Page 24 and 25: 20 [R] predict . . . . . . . . . .
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
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Subject index 93 c(minbyte) c-class
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Subject index 95 choice models, con
Page 101 and 102:
Subject index 97 cointegration, con
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Subject index 99 constrained estima
Page 105 and 106:
Subject index 101 cumsp command, [T
Page 107 and 108:
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
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Subject index 121 invfft() function
Page 127 and 128:
Subject index 123 label values, [P]
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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,
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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
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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
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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
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Subject index 163 variables, contin
Page 169 and 170:
Subject index 165 weights, continue
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