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Stata offers everything you need for reproducible data analysis: powerful statistical functions, visualizations, data management, and automated reporting—all on one intuitive platform.

New Features in Stata 19
Stata 19: Machine Learning with H2O, Gradient Boosting, and Random Forest for Regression and Classification

Machine learning via H2O: Ensemble decision trees

Stata 19: Bayesian quantile regression for estimating posterior distributions of quantile regression coefficients

Bayesian quantile regression

Stata 19: Conditional Average Treatment Effects (CATE) for Analyzing Different Treatment Effects for Individuals and Groups

Conditional average treatment effects (CATE)

Stata 19: Statistical Inference in Instrumental Variables Models with Weak Instruments

Inference robust to weak instruments

Stata 19: High-dimensional fixed effects for accounting for multiple high-dimensional categorical variables in regression models

High-dimensional fixed effects (HDFE)

Stata 19: Structural Vector Autoregression (SVAR) Models with Instrumental Variables for Analyzing Dynamic Causal Effects

SVAR models via instrumental variables

Stata 19 enables Bayesian variable selection in linear regression models. This process selects relevant explanatory variables and takes into account uncertainties in variable selection. Among others, global-local shrinkage priors and spike-and-slab priors are available for the regression coefficients.

Bayesian variable selection for linear model

Stata 19: Correlated random-effects model for panel data with time-varying and time-invariant variables

Correlated random-effects (CRE) model

Stata 19: Control-function linear and probit models for analyzing endogenous variables and testing for endogeneity

Control-function linear and probit models

Stata 19: Marginal interval-censored multiple-event Cox model for analyzing multiple events with unknown exact onset times

Interval-censored multiple-event Cox model

Stata 19: Panel Data Vector Autoregressive Model for Analyzing the Temporal Trends of Correlated Variables in Panel Data

Panel-data vector autoregressive model

Stata 19: Bayesian Bootstrap and Replication Weights for Estimating Statistical Parameters

Bayesian bootstrap

Stata 19: New graphical features, including heat maps, range plots, and bar charts with confidence intervals

Graphics: Bar graph CIs, heat maps, ...

Stata 19: Instrumental-variable local-projection impulse-response functions for analyzing dynamic causal effects

Instrumental-variables local-projection IRFs

Meta-analysis for Correlations in Stata 19: Summarizing Correlation Coefficients from Multiple Studies

Meta-analysis for correlations

Stata 19: Multiple datasets: Modify a set of frames. Edit framesets without loading them into memory.

Multiple datasets: Modify a set of frames

Mundlak specification test in Stata 19 for choosing between random-effects, fixed-effects, and correlated-random-effects models.

Mundlak specification test

Bayesian asymmetric Laplace model in Stata 19 for Bayesian quantile regression with skewed distributions, as well as simultaneous, multilevel, and nonlinear models.

Bayesian asymmetric Laplace model

Stata 19 Do-file Editor with autocomplete, templates, code folding, and highlighting of words and parentheses.

Do-file Editor: Autocompletion, templates, ...

Stata 19: Improved table functions for tabulating and exporting statistical results

Tables: Easier tabulations, exporting, ...

View all Stata features
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