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ERM	=	Endogeneity
	+	Selection
	+	Treatment

Latent class analysis (LCA)

Discover and understand the unobserved groupings in your data. Use LCA's model-based classification to find out

• how many groups you have,
• who is in those groups, and
• what makes those groups distinct.

bayes: logistic ...
and 44 more

Type bayes: in front of any of 45 Stata estimation commands to fit a Bayesian regression model.

Markdown & dynamic documents

Type this,

×

Get this,

• Create webpages from Stata
• Intermix text, regressions, results, graphs, etc.
• See changes in data or commands automatically reflected on webpage

Linearized DSGEs

Write your model in simple algebraic form. Stata does the rest: solve model, estimate parameters, estimate policy and transition matrices (with CIs), estimate and graph IRFs, and perform forecasts.

Finite mixture models (FMMs)

• 17 estimators and combinations
• Continuous, binary, count, ordinal, categorical, censored, and truncated outcomes
• Survival outcomes

Spatial autoregressive models

Because


         sometimes


                  where you are


                                  matters.

Interval-censored survival models

Fit any of Stata's six parametric survival models to interval-censored data. All the usual survival features are supported: stratified estimation, robust and clustered SEs, survey data, graphs, and more.

Nonlinear multilevel
mixed-effects models

      When ...
      your science ...
      says ...
      your model ...
      is ...
      nonlinear in its parameters

Mixed logit models: Advanced choice modeling

Do you walk to work, ride a bus, or drive your car? Which of three insurance plans do you buy? Which political party do you vote for?

We make dozens of choices every day. Researchers have access to gaggles of data about those choices. Mixed logit introduces random effects into choice modeling and thereby relaxes the IIA assumption and increases model flexibility.

Nonparametric regression

When you know something matters.

But have no idea how.

Create Word documents from Stata

• Automate your reports
• Write paragraphs and tables to Word documents
• Embed Stata results and graphs in paragraphs and tables
• Customize formatting of text, tables, and cells

Create PDFs, too!

Bayesian multilevel models

×

Small number of groups?
Many hierarchical levels?
Prefer making probability statements?

Consider Bayesian multilevel modeling.

Threshold regression

Your time-series regression may change parameters at some point in time or at multiple points in time. The activity of foraging animals might follow a completely different pattern at temperatures above some threshold. You may not know the value of that threshold. Finding such thresholds and estimating the parameters within the regimes is what threshold regression does.

Panel-data tobit with random coefficients

Stata has long had estimators for random effects (random intercepts) in panel data.

Now you can have random coefficients, too.

Search, browse, and import FRED data

The St. Louis Federal Reserve makes available over 470,000 U.S. and international economic and financial time series. You can now easily search, browse, and import these data.

Multilevel regression for interval-measured outcomes

Incomes are sometimes recorded in groupings, as are people's weights, insect counts, grade-point averages, and hundreds of other measures. Often we have repeated measurements for individuals, or schools, or orchards, etc. So ... we need multilevel regression for interval-measured (interval-censored) outcomes.

Multilevel tobit regression for censored outcomes

• Left-censoring, right-censoring, both
• Censoring that varies by observation
• Make inferences about either the uncensored or the censored outcome
• Robust and clustered SEs
• Support for survey data

Panel-data cointegration tests

• Tests
  • Kao
  • Pedroni
  • Westerlund
• Total of nine variants of tests

Tests for multiple breaks in time series

• Cumulative sum (CUSUM) test for parameter stability
  • CUSUM of recursive residuals
  • CUSUM of OLS residuals
• Plots with CIs

Multiple-group generalized SEM

Generalized SEM now supports multiple-group analysis. Easily specify groups and test parameter invariance across groups. GSEM models include

• continuous, binary, ordinal, count, categorical, and even survival outcomes
• multilevel models

ICD-10-CM/PCS

• NCHS's ICD-10-CM diagnosis codes
• CMS's ICD-10-PCS procedure codes
• Verify codes are valid
• Create new variables based on codes

Power for cluster randomized designs

Power analysis for comparing

• One- and two-sample means
• One- and two-sample proportions
• Two-sample survivor curves

when you randomize clusters instead of individuals

Power for linear regression models

×

• Solve for
  • Power
  • Sample size
  • Effect size
• Specify lists of
  • Alpha values
  • Power levels
  • Beta values
  • Sample sizes
  • And more
• Automated tables and graphs

Heteroskedastic linear regression

• Model for the variance
• Robust and cluster SEs
• Survey-data support

Poisson models with sample selection

Counts are common. How many:

Outcomes are not always seen.

So you need Poisson models with sample selection.

Of course, many of the new features are highly parallelized.