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Lasso is a machine-learning technique used for model selection, prediction, and inference.
Explore and combine the results from different studies
A new, unified suite of commands for modeling choice data.
Stata's commands for report generation allow you to create complete Word®, Excel®, PDF, and HTML documents that include formatted text, as well as summary statistics, regression results, and graphs produced by Stata.
Embed and execute Python code from within Stata.
Frames - Multiple datasets in memory
Work with multiple datasets in memory. Link datasets and access variables in the datasets in memory.
Bayesian - Multiple chains
Bayesian inference based on an MCMC (Markov chain Monte Carlo) sample is valid only if the Markov chain has converged.
Bayesian - Predictions
These predictions are useful for checking model fit and for predicting out-of-sample observations.
Panel data ERMs
Fit models that account for three common problems that arise in observational data—endogenous covariates, sample selection, and treatment—either alone or in combination.
Sample-size analysis for confidence intervals
The new ciwidth command performs Precision and Sample Size (PrSS) analysis, which is sample-size analysis for confidence intervals (CIs).
Import from SAS
You can now import data stored in SAS (.sas7bdat). Dialog boxes make it easy to explore the data before importing.
Import from SPSS
You can now import data stored in SPSS (.sav). Dialog boxes make it easy to explore the data before importing.
Nonparametric series regression
Fit nonparametric series regressions that approximate the mean of the dependent variable using polynomials, B-splines, or splines of the covariates
Nonlinear DSGE models
The dsgnl command fits nonlinear DSGE models, which means that you no longer need to linearise the models first.
Multiple-group IRT models
The irt commands allow comparisons across groups. Take any of the existing irt commands, add a group(varname) option, and fit the corresponding multiple-group model.
Panel-data mixed logit
Sometimes individuals make the same decision repeatedly. This is panel data. Relaxes the independence of irrelevant alternatives assumption.
Missing data is sometimes not random. Heckman selections help fit two level panel data.
Nonlinear mixed effects models with lags, leads and differences
New features for fitting nonlinear mixed-effects models (NLMEMs) that may include lag, lead (forward), and difference operators