Time series
Handle the statistical challenges inherent to time-series data—autocorrelations, common factors, autoregressive conditional heteroskedasticity, unit roots, cointegration, and much more. Analyze univariate time series using ARIMA, ARFIMA, Markov-switching models, ARCH and GARCH models, and unobserved-components models. Compare ARIMA or ARFIMA models using AIC, BIC, and HQIC, and select the best number of autoregressive and moving-average terms. Analyze multivariate time series using VAR, structural VAR, instrumental-variables (proxy) structural VAR, VEC, multivariate GARCH, dynamic-factor models, and state-space models. Compute and graph impulse responses. Estimate IRFs via local projections. Test for unit roots. Perform Bayesian time-series analysis.

Forecasting
Build multiequation models, and produce forecasts of levels, trends, rates, etc. Whether you have a small model with a few equations or a complete model of the economy with thousands of equations, Stata can help you build that model and produce forecasts. Your model can include both estimated relationships and known identities. You can easily create and compare forecasts under different scenarios, create static and dynamic forecasts, and even estimate stochastic confidence intervals. You can create your model by using an intuitive command syntax or by using the interactive forecasting control panel.

Panel data
Take full advantage of the extra information that panel data provide while simultaneously handling the peculiarities of panel data. Study the time-invariant features within each panel, the relationships across panels, and how outcomes of interest change over time. Fit linear models or nonlinear models for binary, count, ordinal, censored, or survival outcomes with fixed-effects, random-effects, or population-averaged estimators. Fit correlated random-effects models. Fit linear models with high-dimensional fixed effects. Fit dynamic models, vector autoregressive models, or models with endogeneity. Fit Bayesian panel-data models.

Bayesian analysis
Perform Bayesian econometrics analysis using one of the Markov chain Monte Carlo (MCMC) methods. You can choose from various supported models, such as panel-data, hierarchical, VAR, and DSGE models, or you can even program your own. Extensive tools are available to check convergence, including multiple chains. Compute posterior mean estimates and credible intervals for model parameters and functions of model parameters. You can perform both interval- and model-based hypothesis testing. Compare models using Bayes factors. Compute model fit using posterior predictive values. Generate predictions and forecasts. If you want to account for model uncertainty in your regression model, use Bayesian model averaging. Use Bayesian variable selection for linear regression to identify predictors important to your outcome and perform Bayesian inference.

Machine learning
With machine learning via H2O, you can use ensemble decision trees—random forests and gradient boosting machines—for regression and classification. Or use lasso for sparse regression and classification. Or use Bayesian variable selection or Bayesian model averaging to select predictors in a linear model. For causal inference with machine learning, use double-selection lasso, partialing-out lasso, and double machine learning. You can use PCA or kmeans, kmedians, or hierarchical clustering for unsupervised learning. And use search to find community-contributed commands for neural networks, support vector machines, graphical lasso, text mining, and more.

Programming
Want to program your own commands to perform estimation, perform data manipulation, or implement other new features? Stata is programmable, and thousands of Stata users have implemented and published thousands of community-contributed commands. These commands look and act just like official Stata commands and are easily installed for free over the Internet from within Stata. A unique feature of Stata's programming environment is Mata, a fast and compiled language with support for matrix types. Of course, it has all the advanced matrix operations you need. It also has access to the power of LAPACK. What's more, it has built-in solvers and optimizers to make implementing your own maximum likelihood, GMM, or other estimators easier. And you can leverage all of Stata's estimation and other features from within Mata. Many of Stata's official commands are themselves implemented in Mata.

PyStata—Python integration
Interact Stata code with Python code. You can seamlessly pass data and results between Stata and Python. You can use Stata within Jupyter Notebook and other IPython environments. You can call Python libraries such as NumPy, matplotlib, Scrapy, scikit-learn, and more from Stata. You can use Stata analyses from within Python.

Import and export data
You can easily import data for your research from many sources. If your research involves data from Federal Reserve Economic Data (FRED) or from Haver Analytics databases, you can directly import data into Stata. You can use any ODBC data source including Oracle, SQL Server, Access, MySQL, and DB2, and you can perform custom SQL queries through ODBC. Or access data from from Wharton Research Data Services (WRDS) via JDBC. Stata also provides support for importing and exporting data in Excel, CSV, SPSS, dBase, and a variety of other file types.

Automated reporting and customizable tables
Stata is designed for reproducible research, including the ability to create dynamic documents incorporating your analysis results. Create Word or PDF files, populate Excel worksheets with results and format them to your liking, and mix Markdown, HTML, Stata results, and Stata graphs, all from within Stata. Create tables that compare regression results or summary statistics, use default styles or apply your own, and export your tables to Word, PDF, HTML, LaTeX, Excel, or Markdown and include them in your reports.

Reproducibility
Stata is the only software for data science and statistical analysis featuring a comprehensive integrated versioning that ensures your code continues to run, unaltered, even after updates or new versions are released. No need to keep around multiple legacy installations to avoid breaking your system; Stata code from 40 years ago can still be run without modification. Datasets, graphs, scripts, programs, and more are 100% cross-platform and backward compatible.