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Political science 

 
Quantitative political scientists rely on Stata because of its breadth, accuracy, and reproducibility. Whether you are researching decentralization/federalism, voting behavior, legislative redistricting, or internal conflict, Stata provides all the statistics, graphics, and data-management tools needed to pursue a broad range of political science questions.



Features for political scientists:

Structural equation models
Construct models for continuous, binary, count, ordinal, multinomial, or survival outcomes and incorporate unobserved components at any level. Specify weights at each level of the model. Use with complex survey data. And more.

 

Multilevel mixed-effects models
Fit fixed- and random-effects mixed-effects models to multilevel data with continuous, binary, count, and survival outcomes. Construct models for different correlation structures and nesting levels. And more.

 

Panel data
Obtain descriptive statistics and estimates for linear, nonlinear, and dynamic panel-data models. Get access to instrumental-variables random-effects, fixed-effects, and population-averaged estimates. Build your own dynamic model or use traditional models like Arellano–Bond. Fit models for binary, count, and continuous outcomes. And more.

 

Cross-sectional models
Fit linear, quantile, truncated, and censored regressions and maximum likelihood models for binary, count, fractional, continuous, ordered, and multivariate outcomes. And more.

 

Treatment effects
Estimate treatment effects for continuous, binary, count, and survival outcomes and for multilevel and multivalued treatments. Obtain estimates of effects under endogeneity. Choose from inverse probability weights (IPW), propensity-score matching, covariate matching, regression adjustment, doubly-robust augmented IPW and IPW with regression adjustment models. And more.

 

Instrumental variables and Selection
Fit cross-sectional and panel-data models under endogeneity and with selection. Use estimators for selection and endogeneity for binary, count, and continuous outcomes. And more.

 

Survival analysis
Perform survival-data analysis for your descriptive statistics, Cox proportional hazards model, linear regression models, structural equation models, binary response models, discrete response models, instrumental-variables models, and regressions models with selection. And more. 

 

Time series
Fit multivariate and univariate time-series models. Obtain ARIMA, GARCH, ARCH, VAR, structural VAR, VEC, multivariate GARCH, multivariate ARCH, dynamic factors, and unobserved-components models. And more. 

 

Forecasting
Compute static and dynamic forecasts using estimation results from time-series, panel, and cross-sectional data. Incorporate results from different estimations, contrast alternative forecast scenarios, write down identities and factor adjustments, and compute stochastic confidence intervals.

 

Multiple imputation
Use descriptive statistics such as means, proportions, and ratios, and fit linear and nonlinear regressions, multilevel mixed-effects models, panel-data models, survival models, and much more using multiple imputation to account for missing data in your sample.

 

Bayesian analysis
Fit Bayesian regression models using a Metropolis–Hastings Markov chain Monte Carlo (MCMC) method. Choose from a variety of supported models or program your own. Check convergence visually using extensive graphical tools. Compute posterior mean estimates and credible intervals for model parameters and functions of model parameters. Perform interval and model-based hypothesis testing. Compare models using Bayes factors.

 

Mata
Program your own estimator using Stata's built-in matrix language, MATA. Use MATA interactively with Stata. Obtain inversions, decompositions, eigenvalues and eigenvectors, and numerical derivatives. Use LAPACK routines, real and complex numbers, string matrices, and object-oriented programming. And more.

 

 

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