text: 
  Combine endogenous covariates,
  sample selection, and endogenous
  treatment in models for
  continuous, binary, ordered, and
  censored outcomes.
  Take your causal inference to a whole new level.
  Discover and understand the unobserved groupings
  in your data. Use LCA's model-based classification
  to find out
  Type bayes: in front of any
  of 45 Stata estimation commands
  to fit a Bayesian regression model.
  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.
  Because
  sometimes
  where you are
  matters.
  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.
  When ...
  your science ...
  says ...
  your model ...
  is ...
  nonlinear in its parameters
  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.
  When you know something matters.
  But have no idea how.
  Small number of groups?
  Many hierarchical levels?
  Prefer making probability statements?
  Consider Bayesian multilevel modeling.
  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.
  Stata has long had estimators
  for random effects (random
  intercepts) in panel data.
  Now you can have random
  coefficients, too.
  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.
  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.
  Generalized SEM now supports
  multiple-group analysis. Easily
  specify groups and test
  parameter invariance across groups.
  GSEM models include
  Power analysis for comparing
  when you randomize clusters instead of individuals
  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.
  You can learn about
  all of Stata and about
  all of Stata's features.