Easily create tables that compare regression results or summary statistics. Create styles and apply them to any table you build, and then export to MS Word®, PDF, HTML, LaTeX, MS Excel®, or Markdown and include them in reports.
Use probabilistic statements to answer economic questions.
Updated algorithms behind sort and collapse
fit difference-in-differences (DID) and difference-in-difference-in-differences or triple-differences (DDD) models
Interval-censored Cox model
The new estimation command stintcox fits the Cox model to interval-censored event-time data.
Perform multivariate meta-analysis to account for the correlation between multiple effect sizes in multiple studies.
Bayesian VAR models
Fit Bayesian vector autoregressive (VAR) models
Bayesian multilevel models: nonlinear, joint, SEM-like, and more
You can fit breadth of Bayesian multilevel models with the new elegant random-effects syntax of the bayesmh command
Treatment-effects lasso estimation
Use telasso to estimate treatment effects and control for many covariates
These plots are useful for assessing heterogeneity of the studies and for detecting potential outliers.
The leave-one-out meta-analysis performs multiple meta-analyses by excluding one study at each analysis.
Bayesian longitudinal / panel-data models
fit random-effects panel-data or longitudinal models by using xtreg for continuous outcomes, xtlogit or xtprobit for binary outcomes, xtologit or xtoprobit for ordinal outcomes, and more. In Stata 17, you can fit Bayesian versions of these models by simply prefixing them with bayes.
Panel-data multinomial logit model
Stata's new estimation command xtmlogit fits panel-data multinomial logit (MNL) models to categorical outcomes observed over time.
Zero-inflated ordered logit model
This model is used when data exhibit a higher fraction of observations in the lowest category than would be expected from a standard ordered logistic model.
Nonparametric tests for trend
The nptrend command now supports four tests for trend across ordered groups. Choose between the Cochran–Armitage test, the Jonckheere–Terpstra test, the linear-by-linear trend test, and the Cuzick test using ranks.
Bayesian dynamic forecasting
Bayesian dynamic forecasts produce an entire sample of predicted values instead of a single prediction as in classical analysis.
Bayesian IRF and FEVD analysis
Impulse–response functions (IRFs), dynamic-multiplier functions, and forecast-error variance decompositions (FEVDs) are commonly used to describe the results from multivariate time-series models such as VAR models. VAR models have many parameters, which may be difficult to interpret. IRFs and other functions combine the effect of multiple parameters into one summary (per time period).
BIC for lasso penalty selection
Use the Bayesian information criterion (BIC) to select the penalty parameter after lasso for prediction and lasso for inference by specifying the selection (bic) option.
Lasso for clustered data
Account for clustered data in your lasso analysis.
Bayesian linear and nonlinear DSGE models
Fit Bayesian linear and nonlinear dynamic stochastic general equilibrium (DSGE) models
Intel Math Kernel Library (MKL)
Stata 17 introduces usage of the Intel Math Kernel Library (MKL) on compatible hardware (all Intel- and AMD-based 64-bit computers), providing deeply optimised LAPACK routines.
New functions for dates and times
Stata 17 has added new convenience functions for handling dates and times in both Stata and Mata. The new functions can be grouped into three categories: durations, relative dates and components.
Do-file Editor improvements
Stata 17 makes navigating do-files easier with the new Navigation Control and bookmarks.
Stata on Apple Silicon
Stata 17 for Mac is available as a universal application that will run natively on both Macs with Apple Silicon and Macs with Intel processors.
Connecting Stata with databases is now even easier. Stata 17 adds support for JDBC (Java Database Connectivity).
Embed and execute Java code directly in Stata
Experiment with connecting to H2O, a scalable and distributed open-source machine-learning and predictive analytics platform.
Invoke Stata from a stand-alone Python environment via a new pystata Python package.
Jupyter Notebook with Stata
Combine the capabilities of both Python and Stata in a single environment to make your work easily reproducible and shareable with others.