Tables

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.

Bayesian Econometrics

Use probabilistic statements to answer economic questions.

Faster Stata

Updated algorithms behind sort and collapse

Difference-in-differences

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.

Multivariate meta-analysis

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

Galbraith plots

These plots are useful for assessing heterogeneity of the studies and for detecting potential outliers.

Leave-one-out meta-analysis

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.

JDBC

Connecting Stata with databases is now even easier. Stata 17 adds support for JDBC (Java Database Connectivity).

Java integration

Embed and execute Java code directly in Stata

H2O integration

Experiment with connecting to H2O, a scalable and distributed open-source machine-learning and predictive analytics platform.

PyStata

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.