Stata 15 help for xt

[XT] xt -- Introduction to xt commands


The xt series of commands provide tools for analyzing panel data (also known as longitudinal data or in some disciplines as cross-sectional time series when there is an explicit time component). Panel datasets have the form x_[it], where x_[it] is a vector of observations for unit i and time t. The particular commands (such as xtdescribe, xtsum, and xtreg) are documented in alphabetical order in the entries that follow this entry. If you do not know the name of the command you need, try browsing the second part of this description section, which organizes the xt commands by topic. Remarks and examples of [XT] xt describes concepts that are common across commands.

The xtset command sets the panel variable and the time variable; see [XT] xtset. Most xt commands require that the panel variable be specified, and some require that the time variable also be specified. Once you xtset your data, you need not do it again. The xtset information is stored with your data.

If you have previously tsset your data by using both a panel and a time variable, these settings will be recognized by xtset, and you need not xtset your data.

If your interest is in general time-series analysis, see [U] 26.13 Time-series models and the Time-Series Reference Manual. If your interest is in multilevel mixed-effects models, see the Multilevel Mixed-Effects Reference Manual. If you are interested in SAR (spatial autoregressive or simultaneously autoregressive) models for panel data, see [SP] spxtregress.


xtset Declare data to be panel data

Data management and exploration tools

xtdescribe Describe pattern of xt data xtsum Summarize xt data xttab Tabulate xt data xtdata Faster specification searches with xt data xtline Panel-data line plots

Linear regression estimators

xtreg Fixed-, between-, and random-effects, and population-averaged linear models xtregar Fixed- and random-effects linear models with an AR(1) disturbance xtgls Fit panel-data models using GLS xtpcse Linear regression with panel-corrected standard errors xthtaylor Hausman-Taylor estimator for error-components models xtfrontier Stochastic frontier models for panel data xtrc Random-coefficients model xtivreg Instrumental variables and two-stage least squares for panel-data models

Unit-root tests

xtunitroot Panel-data unit-root tests

Cointegration tests

xtcointtest Panel-data cointegration tests

Dynamic panel-data estimators

xtabond Arellano-Bond linear dynamic panel-data estimation xtdpd Linear dynamic panel-data estimation xtdpdsys Arellano-Bover/Blundell-Bond linear dynamic panel-data estimation

Censored-outcome estimators

xttobit Random-effects tobit models xtintreg Random-effects interval-data regression models

Binary-outcome estimators

xtlogit Fixed-effects, random-effects, and population-averaged logit models xtprobit Random-effects and population-averaged probit models xtcloglog Random-effects and population-averaged cloglog models

Ordinal-outcome estimators

xtologit Random-effects ordered logistic models xtoprobit Random-effects ordered probit models

Count-data estimators

xtpoisson Fixed-effects, random-effects, and population-averaged Poisson models xtnbreg Fixed-effects, random-effects, & population-averaged negative binomial models

Survival-time estimators

xtstreg Random-effects parametric survival models

Generalized estimating equations estimator

xtgee Population-averaged panel-data models using GEE


quadchk Check sensitivity of quadrature approximation


An xt dataset:

pid yr_visit fev age sex height smokes ---------------------------------------------- 1071 1991 1.21 25 1 69 0 1071 1992 1.52 26 1 69 0 1071 1993 1.32 28 1 68 0 1072 1991 1.33 18 1 71 1 1072 1992 1.18 20 1 71 1 1072 1993 1.19 21 1 71 0

The other xt commands need to know the identities of the variables identifying patient and time. You could type

. xtset pid yr_visit

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