New in Stata 12
Stata 12 now shipping. Highlights are shown below. Select any header to find out more.
PDF of the Stata 12 announcement—click here.
Also visit Why use Stata? and details of Stata’s capabilities.
- Path diagrams
- Graphical model builder
- Standardized and unstandardized estimates
- Modification indices
- Direct and indirect effects
- Score tests and Wald tests
- Factors scores and other predictions
- Goodness of fit
- Estimation with groups and tests of invariance
- Survey data and clustered data
- Raw or statistical summary data
- FIML estimation with missing at random (MAR) data
- Maximum likelihood, ADF, and GMM estimation
- Flexible extension of multivariate regression, instrumental variables, and simultaneous systems
- Confirmatory factor analysis (CFA), correlated uniqueness models, latent growth models, multiple indicators and multiple causes (MIMIC), ...
- More ...
- Compare reference or adjacent categories
- Compare to grand mean
- Orthogonal polynomials
- Treatment effects
- More ...
- Compare means, intercepts, or slopes
- Compare odds ratios
- Bonferroni, Scheffé, Tukey, Dunnett, and other adjustments
- More ...
- Profile and interaction plots
- Margins, contrasts, and pairwise comparisons
- Potential outcomes
- Comparative graphs
- More ...
- Parametric and nonparametric
- Adjustments for covariates
- Case-control regression models
- Bootstrap and model-based SEs
- Area under the curve (AUC) and partial AUC
- More ...
- Chained equations
- Conditional imputation
- Impute separately within groups
- Linear and nonlinear predictions
- Measure simulation error
- Panel data and multilevel models
- Impute continuous, ordinal, cardinal, and count variables
- More ...
- Complex survey data
- Frequency and sampling weights
- Robust and clustered SEs
- Weighting at each level
- Residual covariance structures: exponential, banded, and Toeplitz
- More ...
- Preview tool
- Adjust import based on preview
- More data management: ODBC connections strings, EBCDIC, rename groups of variables, ...
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- Trend, seasonal, and cyclical components
- Static and dynamic forecasts of components
- Stochastic cycles
- More ...
- Automatically adjusts to dataset size
- Tunable
- Up to 1 terabyte of memory
- More...
- Long-memory processes
- Fractional integration
- Robust variance estimates
- Static and dynamic forecasts
- Linear constraints
- More ...
- Manage variables, storage types, notes, and formats without leaving the main interface
- Select variables using filters
- Filter prior commands and search results
- Tabbed Viewer
- Jump to dialogs, related commands, and sections in the online help
- Hide, show, reorder, and filter variables in the Data Editor
- Preview before pasting data
- PDF export of results and graphs
- New Mac interface
- More ...
- Constant conditional correlations (CCC)
- Dynamic conditional correlations (DCC)
- Varying conditional correlations (VCC)
- Multivariate normal and Students’ t errors
- Robust variance estimates
- Level and variance predictions
- Static and dynamic forecasts
- More ...
- Parametric estimates after ARIMA, ARFIMA, and UCM
- Assess importance of frequencies
- More ...
- Downloadable tool
- Report for submission to regulatory agencies
- More ...
- Trend and cycle decompositions
- Christiano–Fitzgerald band-pass filter
- Baxter–King band-pass filter
- Hodrick–Prescott high-pass filter
- Butterworth high-pass filter
- More ...
- More estimators
- Up to 64 cores
- More ...
- Trading days
- User definable
- Lags and leads using business days
- Conversions from standard calendar
- More ...
- General statistics: functions for Tukey's Studentized range and Dunnett's multiple range, baseline odds for logistic regression, ...
- Survey data: support for SEM, bootstrap and successive difference replicate (SDR) weights, goodness of fit after binary models, coefficient of variation, ...
- Count data: truncated count-data regressions, probability predictions, robust and cluster-robust SEs for fixed-effects Poisson regression, ...
- Panel data: probability predictions, multiple imputation support, ...
- Survival data: goodness-of-fit statistic that is robust to censoring
