Extended regression models (ERMs)
Combine endogenous covariates,sample selection, and endogenous treatment in models for continuous, binary, ordered, and censored outcomes.
潜在类别分析Latent class analysis(LCA)
Discover and understand the unobserved groupings in your data. Use LCA's model-based classification to find out
Bayesian regression models using the bayes prefix
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.
有限混合模型Finite mixture models (FMMs)
Spatial autoregressive models
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.
非线性多层混合效应模型Nonlinear multilevel mixed-effects models
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.
When you know something matters.
But have no idea how.
Bayesian multilevel models
Small number of groups? Many hierarchical levels? Or simply like the graph above?
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.
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
Panel-data cointegration tests
Tests for multiple breaks in time series
Multiple-group generalized SEM
Generalized SEM now supports multiple-group analysis. Easily specify groups and test parameter invariance across groups. GSEM models include
Power for cluster randomized designs
Power analysis for comparing
when you randomize clusters instead of individuals
Power for linear regression models
Heteroskedastic linear regression
Poisson models with sample selection
Counts are common. How many:
Fish did you catch?
Outcomes are not always seen.
Folks evade the game warden.
So you need Poisson models with sample selection.
More in panel data
More in graphics
More in statistics
More in the interface
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