Bayesian model averaging (BMA)
Causal mediation analysis
Tables of descriptive statistics
Group sequential designs
Robust inference for linear models
Wild cluster bootstrap
Flexible demand systems
TVCs with interval-censored Cox model
GOF plots for survival models
Lasso for Cox model
Heterogeneous DID
Multilevel meta-analysis
Meta-analysis for prevalence
Local projections for IRFs
Model selection for ARIMA and ARFIMA
RERI
New spline functions
Corrected and consistent AICs
IV fractional probit model
IV quantile regression
All-new graph style
Graph colors by variable
Alias variables across frames
Frame sets
Boost-based regular expressions
Vectorized numerical integration
New reporting features
Do-file Editor enhancements
Data Editor enhancements
More
Bayesian model averaging (BMA)
Causal mediation analysis
Tables of descriptive statistics
Group sequential designs
Robust inference for linear models
Wild cluster bootstrap
Flexible demand systems
TVCs with interval-censored Cox model
Lasso for Cox model
GOF plots for survival models
RERI
New spline functions
Corrected and consistent AICs
IV fractional probit model
IV quantile regression
Heterogeneous DID
Multilevel meta-analysis
Meta-analysis for prevalence
Local projections for IRFs
Model selection for ARIMA and ARFIMA
Alias variables across frames
Frame sets
Boost-based regular expressions
Vectorized numerical integration
Data Editor enhancements
Do-file Editor enhancements
New reporting features
Graph colors by variable
All-new graph style
More
Bayesian model averaging (BMA)
Causal mediation analysis
Tables of descriptive statistics
Heterogeneous DID
Group sequential designs
Multilevel meta-analysis
Meta-analysis for prevalence
Robust inference for linear models
Wild cluster bootstrap
Local projections for IRFs
Model selection for ARIMA and ARFIMA
Flexible demand systems
TVCs with interval-censored Cox model
Lasso for Cox model
GOF plots for survival models
RERI
IV quantile regression
IV fractional probit model
Corrected and consistent AICs
New spline functions
Alias variables across frames
Frame sets
Boost-based regular expressions
Vectorized numerical integration
New reporting features
Do-file Editor enhancements
Data Editor enhancements
Graph colors by variable
All-new graph style
More
High-dimensional fixed effects (HDFE)
Bayesian variable selection for linear model
Interval-censored multiple-event Cox model
LatestInference robust to weak instruments
Bayesian quantile regression
Panel-data VAR model
Correlated random-effects (CRE) model
Control-function linear and probit models
LatestSVAR models via instrumental variables
IV local-projection IRFs
Bayesian asymmetric Laplace model
Meta-analysis for correlations
Mundlak specification test
Do-file Editor: Autocompletion, templates, and more
LatestColors by variable for more graphs
More
High-dimensional fixed effects (HDFE)
Bayesian variable selection for linear model
Interval-censored multiple-event Cox model
LatestMeta-analysis for correlations
Bayesian quantile regression
Panel-data VAR model
Inference robust to weak instruments
Mundlak specification test
Correlated random-effects (CRE) model
Control-function linear and probit models
LatestSVAR models via instrumental variables
IV local-projection IRFs
Bayesian asymmetric Laplace model
Colors by variable for more graphs
Do-file Editor: Autocompletion, templates, and more
LatestMore
High-dimensional fixed effects (HDFE)
Bayesian variable selection for linear model
Interval-censored multiple-event Cox model
LatestMeta-analysis for correlations
Correlated random-effects (CRE) model
Control-function linear and probit models
LatestBayesian quantile regression
Bayesian asymmetric Laplace model
Panel-data VAR model
Inference robust to weak instruments
SVAR models via instrumental variables
IV local-projection IRFs
Mundlak specification test
Do-file Editor: Autocompletion, templates, and more
LatestColors by variable for more graphs
More
Uncertain which predictors to use in your regression?
Use Bayesian model averaging to account for this uncertainty in your analysis. Explore influential models and predictors, obtain better predictions, and more.
Causal analysis quantifies causal effects. Causal mediation analysis disentangles them.
Are these effects mediated through another variable? Estimate direct and indirect effects. Calculate the proportion mediated.
Estimate treatment effects that vary over groups and time. Fit models for repeated cross-sectional or panel data.
Visualize effects. Aggregate effects within group, time, or exposure to treatment.
White background • Horizontal y-axis labels • Bright color palette • Side legend • And more
You can also graph colors by variable.
Create tables of descriptive statistics more easily with the new dtable command!
Export to Word, Excel, PDF, LaTeX, HTML, Markdown, and more.
Use variables from multiple datasets as if they exist in one.
And you can now work with frame sets.
Calculate efficacy and futility stopping bounds for clinical trials. Find required sample sizes for interim and final analyses when testing proportions, means, or survivor functions.
Do your studies have effect sizes nested within multiple grouping levels? Use multilevel meta-analysis to account for possible dependence among the effect sizes when combining results.
You asked, we delivered! Perform meta-analysis for proportion or prevalence. Produce forest plots. Explore heterogeneity. Perform subgroup analysis. And more.
Stata's robust features for linear models became even more robust. Learn how.
Small number of clusters? Unequal observations per cluster? No problem! Wild cluster bootstrap handles them all.
Incorporate time-varying covariates in your interval-censored Cox analysis, including prediction and plots of survivor and other functions!
Select variables in a Cox model using lasso and elastic net.
Compute predictions. Graph survivor, failure, and other functions.
Want to know whether your survival model fits your data well? estat gofplot makes this easy. Use it with right-censored and interval-censored data, parametric and semiparametric models, and more.
Estimate impulse–response functions (IRFs) via local projections. Test hypotheses of multiple IRF coefficients. Graph IRFs, orthogonalized IRFs, and dynamic multipliers.
Compare potential ARIMA or ARFIMA models using AIC, BIC, and HQIC. Select the best number of autoregressive and moving-average terms.
Estimate demand for a basket of goods. Evaluate sensitivity to price and expenditure changes. Choose from eight demand systems, including Cobb–Douglas, translog, AIDS, and QUAIDS.
Estimate effects of covariates on quantiles of the outcome's conditional distribution. Account for endogeneity. Plot coefficients across quantiles.
Modeling a proportion or rate?
Have endogenous covariates?
Fit your model with ivfprobit.
Data Editor—Pinnable rows and columns, tooltips for truncated text, variable labels in headers, much more.
Do-file Editor—Automatic backups and syntax highlighting for user-defined keywords.
Compare models using consistent AIC (CAIC). Or, with small sample size, use corrected AIC (AICc).
Revamped spline generation tool—new makespline—supports B-splines and generates splines for multiple variables at once.
Approximate multiple numerical integrals simultaneously. Adaptive Gauss–Kronrod and Simpson methods. Robustness to singular points.
Regular expression functions now use Boost.
More features.
More functions.
putdocx: Bookmarks in paragraphs and tables, image text for voice software, and SVG images in Word.
putexcel: Freeze worksheets, add page breaks, include hyperlinks, and insert headers/footers in Excel.
Export to SPSS.
Bacon treatment-effect decomposition.
Robust SEs for sureg and reg3.
