Stata BE

Trialversion of Stata

The producer provides a free 30-day trialversion on their website. The trialversion contains all the features of Stata. You can register for this license simply by visiting the following link: http://www.stata.com/customer-service/evaluate-stata/

Compatible operating systems

Stata will run on the platforms listed below. While Stata software is platform-specific, your Stata license is not; therefore, you need not specify your operating system when placing your order for a license.

 running Stata on a dual-core, multicore, or multiprocessor computer.

Platforms

Stata for Windows®

• Windows 10 *
• Windows 8 *
• Windows Server 2019, 2016, 2012 *

* Stata requires 64-bit Windows for x86-64 processors made by Intel® or AMD
 

Stata for Mac®

• Mac with Apple Silicon or 64-bit Intel processor
• macOS 11.0 (Big Sur) or newer for Macs with Apple Silicon and macOS 10.12 (Sierra) or newer for Macs with 64-bit Intel processors

Stata for Linux

• Any 64-bit (x86-64 or compatible) running Linux
• For xstata, you need to have GTK 2.24 installed

 

Hardware requirements

Package	Memory	Disk space
Stata/MP	4 GB	2 GB
Stata/SE	2 GB	2 GB
Stata/BE	1 GB	2 GB

Stata for Linux requires a video card that can display thousands of colors or more (16-bit or 24-bit color)

 

 

 

 

 

 

What's new in Stata?   Tables Customize your tables of Summary statistics Results from hypothesis tests Regression results LR and Wald tests, GOF statistics, ... Results from any Stata command Export to Word, Excel LaTeX HTML, Markdown PDF and more   Bayesian econometrics Bayesian VAR models IRF and FEVD analysis Dynamic forecasting Panel/longitudinal-data models Linear and nonlinear DSGE models         PyStata—Python and Stata Call Python from Stata. Call Stata from Python. Exchange data, metadata, and results seamlessly. Use Stata from Jupyter Notebook, Spyder, PyCharm IDE, and more.   Jupyter Notebook with Stata Invoke Stata and Mata from Jupyter Notebook. Easily reproduce your work and collaborate with others. Access results from Stata analyses within Python. Stata output, graphs, and tables seamlessly integrate with your Jupyter Notebook.         Difference-in-differences (DID) and DDD models Evaluate the effect of a policy, a treatment, or an intervention. Control for confounding unobserved group and time characteristics. Use panel data or repeated cross-sections. Use DID. In vogue since 1855.   Faster Stata Stata is fast, and keeps getting faster. Faster sort and collapse Faster mixed models Faster estimation commands Faster import delimited And more         Interval-censored Cox model You want to model time to an event. But you don't know the exact event times—only the intervals in which events happen. And you don't want to make parametric assumptions. Try an interval-censored Cox model.   Multivariate meta-analysis Do you have multiple effect sizes? Do they share a common control group? Do they share the same group of subjects?   Multivariate meta-analysis can help.         Bayesian VAR models You fit your VAR models with var. You fit your Bayesian regression models with bayes:. Now fit your Bayesian VAR models with bayes: var.   Bayesian multilevel modeling Nonlinear, joint, SEM-like, and more. More multilevel models.                 More powerful. Easier to use.         Treatment-effects lasso estimation When you want: Causal inference, average treatment effects, potential-outcome means, double-robust estimation And you have: Many (maybe hundreds or thousands of) potential covariates Use treatment-effects estimation with lasso variable selection.   New functions for dates and times Calculate durations, such as ages and other differences between datetimes. Calculate relative dates, or dates from other dates, such as the previous or next birthday or anniversary relative to a reference date. Extract individual components from datetime values and variables.         Leave-one-out meta-analysis Are there influential studies in your data? Use leave-one-out meta-analysis to find out.   Galbraith plots Graphically summarize meta-analysis results Study-specific effect sizes Precision of effect sizes Overall effect size Detect potential outliers Assess heterogeneity         Panel-data multinomial logit model You can model categorical outcomes with mlogit. You can model panel data with xt. Now you can do both! Stata's new xtmlogit command models categorical outcomes that change over time.   Bayesian panel-data models Bayesian analysis lets you answer probabilistic questions with panel-data models. How likely is it that an extra year of schooling will increase wages? What is the probability of default for a low-risk portfolio? Incorporate prior knowledge, see posterior distributions of random effects, compute Bayesian predictions, and more.         Zero-inflated ordered logit model Need to model an ordinal outcome? Have excess zeros (or responses in the lowest category)? ziologit is the answer.   Nonparametric tests for trend Do responses have an increasing or decreasing trend? Find out using one of four nonparametric tests for trend: Cochran–Armitage test Jonckheere–Terpstra test Linear-by-linear test Cuzick's test with ranks         Bayesian IRF and FEVD analysis What is the effect of a shock over time? What is the mean or median of the effect for a distribution of probable scenarios? Bayesian IRF analysis answers these and more.   Bayesian dynamic forecasting After VAR, you want a dynamic forecast. After Bayesian estimation, you want statistics of posterior distributions. Estimate both. Visualize both.         Lasso with clustered data Your data have ...     many variables. Your data have ...     clusters of observations. Your lasso for prediction, model selection, or inference can now select variables while accounting for clustering. BIC for lasso penalty selection Which variables should lasso include? BIC for lasso penalty selection can tell you.         Bayesian linear and nonlinear DSGE models Forming rational expectations of the future is hard. DSGE models include these expectations. Prior information helps.   Do-file Editor enhancements Persistent bookmarks Navigation Control Syntax highlighting for Java, XML, and more Auto-completion for quotes, parentheses, and brackets         Stata on Apple Silicon Native M1 processor support Universal application for both Intel and Apple Silicon Macs One license, both kinds of hardware   Intel Math Kernel Library (MKL) Mata functions and operators use heavily optimized LAPACK routines underpinned by the Intel Math Kernel Library. Use your favorite Stata commands like always; underlying functions are faster, so you get results faster.         Java integration Use Java interactively (like JShell) from within Stata. Embed Java code in do-files. Embed Java code in ado-files. Compile and execute Java code "on the fly" without external programs. H2O integration Start a new H2O cluster or connect to an existing one. Manipulate data on an H2O cluster. Access the capabilities of H2O directly in Stata.         JDBC Connecting Stata to databases is now easier. Want to access data from Oracle, MySQL, Amazon Redshift, Snowflake, Microsoft SQL Server, and others? Use jdbc. Want one driver that works on Windows, Mac, and Linux? Use jdbc.         Search, browse, and import FRED data The St. Louis Federal Reserve makes available over 470,000 U.S. and international economic and financial time series. You can now easily search, browse, and import these data.

Multilevel regression for interval-measured outcomes Incomes are sometimes recorded in groupings, as are people's weights, insect counts, grade-point averages, and hundreds of other measures. Often we have repeated measurements for individuals, or schools, or orchards, etc. So ... we need multilevel regression for interval-measured (interval-censored) outcomes.

Multilevel tobit regression for censored outcomes Left-censoring, right-censoring, both Censoring that varies by observation Make inferences about either the uncensored or the censored outcome Robust and clustered SEs Support for survey data

Panel-data cointegration tests Tests Kao Pedroni Westerlund Total of nine variants of tests

Tests for multiple breaks in time series Cumulative sum (CUSUM) test for parameter stability CUSUM of recursive residuals CUSUM of OLS residuals Plots with CIs

Multiple-group generalized SEM Generalized SEM now supports multiple-group analysis. Easily specify groups and test parameter invariance across groups. GSEM models include continuous, binary, ordinal, count, categorical, and even survival outcomes multilevel models

ICD-10-CM/PCS NCHS's ICD-10-CM diagnosis codes CMS's ICD-10-PCS procedure codes Verify codes are valid Create new variables based on codes

Power for cluster randomized designs Power analysis for comparing One- and two-sample means One- and two-sample proportions Two-sample survivor curves when you randomize clusters instead of individuals

Power for linear regression models Solve for Power Sample size Effect size Specify lists of Alpha values Power levels Beta values Sample sizes And more Automated tables and graphs

Heteroskedastic linear regression Model for the variance Robust and cluster SEs Survey-data support

Poisson models with sample selection Counts are common. How many: Fish did you catch? Accidents occurred? Patents does a firm generate? Outcomes are not always seen. Folks evade the game warden. Accidents are not always reported. Some firms prefer trade secrets to patents. So you need Poisson models with sample selection.

More in panel data Nonlinear models with random effects, including random coefficients Bayesian panel-data models Interval regression with random intercepts and random coefficients

More in graphics Transparency in graphs SVG export

More in statistics Bayesian survival models Zero-inflated ordered probit Add your own power and sample-size methods Bayesian sample-selection models And yet more

More in the interface Stata in Swedish Stata in Chinese Improvements to the Do-file Editor

And, even more

Stream random-number generator Improvements for Java plugins

 

The whole feature list you will find under the following link:

https://www.stata.com/features/

 

Stata Features

Data management

data transformations, match-merge, ODBC, XML, by-group processing, append files, sort, row–column transposition, labeling, saving results

Basic statistics

summaries, cross-tabulations, correlations, t tests, equality-of-variance tests, tests of proportions, confidence intervals, factor variables

Linear models

regression; bootstrap, jackknife, and robust Huber/White/sandwich variance estimates; instrumental variables; three-stage least squares; constraints; quantile regression; GLS

Multilevel mixed-effects models

generalized linear models;continuous, binary, and count outcomes; two-, three-, and higher-level models; random-intercepts; random-slopes; crossed random effects; BLUPs of effects and fitted values; hierarchical models; residual error structures; support for survey data in linear models

Binary, count, and discrete outcomes

logistic, probit, tobit; Poisson and negative binomial; conditional, multinomial, nested, ordered, rank-ordered, and stereotype logistic; multinomial probit; zero-inflated and left-truncated count models; selection models; marginal effects

Longitudinal data/panel data

random and fixed effects with robust standard errors; linear mixed models, random-effects probit, GEE, random- and fixed-effects Poisson, dynamic panel-data models, and instrumental-variables regression; panel unit-root tests; AR(1) disturbances

Generalized linear models (GLMs)

ten link functions, user-defined links, seven distributions, ML and IRLS estimation, nine variance estimators, seven residuals

Nonparametric methods

Wilcoxon-Mann-Whitney, Wilcoxon signed ranks and Kruskal-Wallis tests; Spearman and Kendall correlations; Kolmogorov-Smirnov tests; exact binomial CIs; survival data; ROC analysis; smoothing; bootstrapping

Exact statistics

exact logistic and Poisson regression, exact case-control statistics, binomial tests, Fisher's exact test for r × c tables

ANOVA/MANOVA

balanced and unbalanced designs; factorial, nested, and mixed designs; repeated measures; marginal means; contrasts

Multivariate methods

factor analysis, principal components, discriminant analysis, rotation, multidimensional scaling, Procrustean analysis, correspondence analysis, biplots, dendrograms, user-extensible analyses

Cluster analysis

hierarchical clustering; kmeans and kmedian nonhierarchical clustering; dendrograms; stopping rules; user-extensible analyses

Resampling and simulation methods

bootstrapping, jackknife and Monte Carlo simulation; permutation tests

Tests, predictions, and effects

Wald tests; LR tests; linear and nonlinear combinations, predictions and generalized predictions, marginal means, least-squares means, adjusted means; marginal and partial effects; forecast models; Hausman tests

Graphics

line charts, scatterplots, bar charts, pie charts, hi-lo charts, regression diagnostic graphs, survival plots, nonparametric smoothers, distribution Q-Q plots

Survey methods

multistage designs; bootstrap, BRR, jackknife, linearized, and SDR variance estimation; poststratification; DEFF; predictive margins; means, proportions, ratios, totals; summary tables; regression, instrumental variables, probit, Cox regression

Survival analysis

Kaplan-Meier and Nelson-Aalen estimators,; Cox regression (frailty); parametric models (frailty); competing risks; hazards; time-varying covariates; left- and right-censoring, Weibull, exponential, and Gompertz analysis

Epidemiology

standardization of rates, case–control, cohort, matched case-control, Mantel-Haenszel, pharmacokinetics, ROC analysis, ICD-9-CM

Time series

ARIMA; ARFIMA; ARCH/GARCH; VAR; VECM; multivariate GARCH; unobserved components model; dynamic factors; state-space models; business calendars; correlograms; periodograms; forecasts; impulse-response functions; unit-root tests; filters and smoothers; rolling and recursive estimation

Multiple imputation

nine univariate imputation methods; multivariate normal imputation; chained equations; explore pattern of missingness; manage imputed datasets; fit model and pool results; transform parameters; joint tests of parameter estimates; predictions

Simple maximum likelihood

specify likelihood using simple expressions; no programming required; survey data; standard, robust, bootstrap, and jackknife SEs; matrix estimators

Programmable maximum likelihood

user-specified functions; NR, DFP, BFGS, BHHH; OIM, OPG, robust, bootstrap, and jackknife SEs; Wald tests; survey data; numeric or analytic derivatives

Other statistical methods

kappa measure of interrater agreement; Cronbach's alpha; stepwise regression; tests of normality

Programming features

adding new commands; command scripting; object-oriented programming; menu and dialog-box programming; Project Manager; plugins

Matrix programming-Mata

interactive sessions, large-scale development projects, optimization, matrix inversions, decompositions, eigenvalues and eigenvectors, LAPACK engine, real and complex numbers, string matrices, interface to Stata datasets and matrices, numerical derivatives, object-oriented programming

Internet capabilities

ability to install new commands, web updating, web file sharing, latest Stata news

Accessibility

Section 508 compliance, accessibility for persons with disabilities

Sample session

A sample session of Stata for Mac, Unix, or Windows.

Community-contributed commands

User-written commands for meta-analysis, data management, survival, econometrics

Graphical user interface

menus and dialogs for all features; Data Editor; Variables Manager; Graph Editor; Project Manager; Do-file Editor; Clipboard Preview Tool; multiple preference sets

Graphics

line charts; scatterplots; bar charts; pie charts; hi-lo charts; contour plots; GUI Editor; regression diagnostic graphs; survival plots; nonparametric smoothers; distribution Q-Q plots

Documentation

20 manuals20 manuals; 11,000+ pages; seamless navigation; thousands of worked examples; methods and formulas; references; 11,000+ pages; seamless navigation; thousands of worked examples; methods and formulas; references

Power and sample size

power; sample size; effect size; minimum detectable effect; means; proportions; variances; correlations; case-control studies; cohort studies; survival analysis; balanced or unbalanced designs; results in tables or graphs

Treatment effects

inverse probability weight (IPW); doubly robust methods; propensity score matching; regression adjustment; covariate matching; multilevel treatments; average treatment effects (ATEs); average treatment effects on the treated (ATETs); potential-outcome means (POMs)

SEM (Structural equation modeling)

graphical path diagram builder; standardized and unstandardized estimates; modification indices; direct and indirect effects; continuous, binary, count, and ordinal outcomes (GLM); multilevel models; random slopes and intercepts; factors scores, empirical Bayes, and other predictions; groups and tests of invariance; goodness of fit; handles MAR data by FIML; correlated data

Functions

statistical; random-number; mathematical; string; date and time

Embedded statistical computations

Numerics by Stata

Contrasts, pairwise comparisons, and margins

compare means, intercepts, or slopes; compare to reference category, adjacent category, grand mean, etc.; orthogonal polynomials; multiple comparison adjustments; graph estimated means and contrasts; interaction plots

GMM an nonlinear regression

generalized method of moments (GMM); nonlinear regression