Movatterモバイル変換

StataNow™
• Power analysis for logistic regressionStataNow
Bayesian analysis
• Bayesian variable selection for linear model
• Bayesian bootstrap and replicate weights
• Bayesian quantile regression
• Bayesian asymmetric Laplace model
• New priors for Bayesian analysis
  • Thebayesmh command andbayes prefix now support the half-Cauchy prior. This heavy-tailed prior is useful for modeling nonnegative parameters that tend to have large values, such as variances and standard deviations.
  • Thebayesmh command andbayes prefix now support the Rayleigh prior. This prior distribution is related to \(\chi^2\) and exponential distributions and thus can be used to model nonnegative parameters with skewed distributions. It is often used in physics and engineering to model parameters—for instance, wind speed—that correspond to a norm of a bivariate standard normal random vector.
• Gibbs sampling for normal linear models with Laplace priors
  • Thebayesmh command with a univariate normal likelihood and Laplace prior for regression coefficients or with a mean parameter of a normal prior and a Laplace hyperprior now supports Gibbs sampling via thegibbs suboption of theblock() option.
• Thebayesmh command with user-defined evaluators has the following new features:
  • bayesmh now provides efficient estimation of random-effects parameters in evaluators by using thereffects subpotion of theblock() option.
  • In addition, theevaluator() andllevaluator() options support thereparameters() suboption, which allows the values of random-effects parameters to be passed to the evaluator as temporary variables. These temporary variables are listed as arguments to the evaluators after the names of temporary scalars and matrices for model parameters specified in theparameters() suboption.
  • bayesmh now supports predictions in evaluators. You may implement predictions in your posterior or likelihood evaluatorsand still be able to use thebayespredict postestimation command afterbayesmh.
  • Specifically, theevaluator() andllevaluator() options support thepredict suboption, which indicates that the evaluator includes code for generating random samples for the outcome.
  • Evaluators inbayesmh are now expected to compute log-likelihood values over the observation sample and return them in a vector form instead of returning the scalar overall log-likelihood value.
Causal inference
• Conditional average treatment effects (CATE)
• Theteffects aipw command for estimating treatment effects via augmented inverse-probability weighting can now provide estimates of the ATETs and can adjust results for sampling weights.
• Thedidregress andxtdidregress commands for difference-in-differences estimation have an improved algorithm to compute confidence intervals via the wild bootstrap.
Additional updates
• Thehdidregress andxthdidregress commands now allow theusercohort option for specifying a known cohort variable rather than allowing these commands to determine cohorts based on the time and group variables.
• The newgencohort command creates a cohort variable to be used withhdidregress andxthdidregress.
Data management
• Multiple datasets: Modify a set of frames
• Thereshape command for converting data from wide to long form and vice versa now defaults to favoring speed instead of favoring memory.
• Thelist command has three new options:
1. sepbyexp(exp) draws a separator line whenever the value of expressionexp changes.exp does not necessarily have to refer to the variables in the dataset.
2. footer displays variable names as a footer.
3. relative displays relative observation numbers when a subset of observations is listed.
Finite mixture models
• Latent class model-comparison statistics
General statistics
• High-dimensional fixed effects (HDFE)
• Bayesian bootstrap and replicate weights
• Control-function linear and probit models
• Inference robust to weak instruments
• More powerful tables for ANOVA and tabulations
• Thenl command for nonlinear least-squares estimaton is now more powerful, with a substitutable expression parser that allows you to define more complicated expressions for linear and nonlinear functions of parameters. Also, it now uses Mata'smoptimize() Gauss–Newton algorithm and Mata'sderiv() function for computing numerical derivatives.
• The generalized method of moments commandgmmis now much faster with thextinstruments() option for specifying panel-styleinstruments and numerical derivatives. The speed is much improved whenthe number of panels is large.
• Theareg command has the newdfabsorb option to adjust degrees offreedom for pairwise collinearity among absorbed variables.
• Theivregress 2slscommand now computes regression coefficients andtheir standard errors by using the Mata orthogonal triangularizationroutinehqrdp, which is more numerically stable than the previous routine.
Graphics
• Bar graph CIs, heat maps, and more
• Colors by variable for more graphs
  • Thecolorvar() option is now available with additional two-way plots:line,connected,tsline,rline,rconnected, andtsrline. This means that you can vary color lines, markers, and more in these plots based on the values of a specified variable.
Interface
• Autocompletion, templates, and more
• Stata in French
• Stata now shows the name of the current (working) frame in the title bar when multiple frames are present. This allows you to easily identify the frame in which any commands issued without aframe prefix will be executed.
Instrumental variables
• Inference robust to weak instruments
• SVAR models via instrumental variables
• Instrumental-variables local-projection IRFs
• Control-function linear and probit models
Linear models
• Conditional average treatment effects (CATE)
• High-dimensional fixed effects (HDFE)
• Bayesian variable selection for linear model
• Correlated random-effects (CRE) model
• Control-function linear model
• Inference robust to weak instruments
• Mundlak specification test
• Panel-data VAR model
• Bayesian quantile regression
• Theareg command has the newdfabsorb option to adjust degrees of freedom for pairwise collinearity among absorbed variables.
• Theivregress 2sls command now computes regression coefficients and their standard errors using the Mata orthogonal triangularization routinehqrdp, which is more numerically stable than the previous routine.
Machine learning
• Machine learning via H2O: Ensemble decision trees
Mata matrix programming
• Stata for Mac on Apple silicon now uses LAPACK/OpenBLAS for numerical computations.
• The new_invmat() function finds the inverse of a square matrix(or the pseudoinverse if the matrix is not invertible).
• The new_solvemat() function solvesAX=B, and it allows you to specify the matrix type, such as lower triangular or symmetric and positive definite, and chooses the suitable solver correspondingly.
• Thederiv() function for numerical derivatives has the following improvements:
  • You can now use the Richardson extrapolation method to compute the numerical derivatives by specifying"richardson" in thederiv_init_technique() function.
  • You can now usederiv_init_tablesize() to specify the table size for computing numerical derivatives by using the Richardson extrapolation method.
• The newlssolve() function solvesAX=B for the minimum-norm solutionX by using the least-squares method.
• The newlsesolve() function solvesAX=c with equality constraintsfor the minimum-norm solutionX using the least-squares method.
• The newlsglmsolve() function finds the solution of a general Gauss–Markov linear model with equality constraints.
• The newst_datalabel() function returns the label of the dataset currently loaded in Stata.
• The newst_datalabel(string scalar name) function sets or resets the label of the dataset currently loaded in Stata.
• The newst_vldir() function returns a string vector of names of all value labels.
Meta-analysis
• Meta-analysis for correlations
Panel data
• High-dimensional fixed effects (HDFE)
• Correlated random-effects (CRE) model
• Panel-data VAR model
• Bayesian panel-data quantile regression
• Mundlak specification test
• Thextreg, fe command has the newdfabsorb option to adjustdegrees of freedom for pairwise collinearity among absorbed variables.
Pharmacokinetics
• Thepkequiv command for performing bioequivalence tests has the following improvements:
• Log-scale analysis of bioequivalence is now performed by default, following guidance from the US FDA and the European EMA.
• Schuirmann's two one-sided tests are now performed by default.
• The newreps() option controls the number of bootstrap replications.
• Additional results are now stored. These includer(limit_table), a matrix of equivalence limits, confidence intervals, and estimates.
Power and sample size
• Power analysis for logistic regressionStataNow
• power andciwidth have new more descriptive labels for table columns that are ratios of two other columns.
Programming
• PyStata enhancements
  The PyStata features for integrating Python into Stata and integrating Stata into Python have the following improvements:
  • When running Stata code within an IPython kernel-based environment, such as Jupyter Notebook and console as well as Jupyter Lab and console, and within other environments that support the IPython kernel, such as Spyder IDE and PyCharm IDE, Stata’s variable names in the current working dataset, macro names, and resultsr(),e(), ands() can now be autocompleted as you type by pressing theTab key.
  • A new%help line magic is now available; it allows you to view the help information of the specified Stata command or topic in the web browser.
  • You can now control whether to echo the Stata commands along with their output when executing them in the Python environment; and you can control whether to display Stata’s output simultaneously when the execution begins or to display the output after Stata finishes execution.
  • In thesfi module, new classBreakError is available; it allows for interrupting Python execution by using theBreak key in Stata.
• TheStata-Python API specification has the following new featuresin theFrame class:
  • The newgetCWF() function returns the name of the current working frame in Stata.
  • The newgetName() function returns the name of the frame.
  • The newgetFrames() function returns the names of all frames in Stata.
• TheStata-Java API specification has the following new features in theFrame class:
• The newgetCWF() function returns the name of the current working frame in Stata.
• The newgetName() function returns the name of the frame.
• The newgetFrames() function returns the names of all frames in Stata.
• Thematrix accum andmatrix vecaccum commands now support samples with one observation.
Reporting
• More powerful tables
Resampling
• Bayesian bootstrap and replicate weights
• You can use the newrwgen command and new options for thebootstrap prefix to implement specialized bootstrap schemes.
  • rwgen generates standard replication and Bayesian bootstrap weights. The command provides two methods:rwgen bsample generates frequency weights by resampling observations, andrwgen bayes generates Bayesian bootstrap weights using the Dirichlet distribution. These weights can be used directly with thebootstrap prefix.
  • bootstrap has newfweights() andiweights() options for performing bootstrap replications using custom weights.fweights() allows users to specify frequency weight variables for resampling, andiweights() lets users provide importance weight variables. These options extendbootstrap's flexibility by allowing user-supplied weights instead of internal resampling, making it easier to implement specialized bootstrap schemes and enhance reproducibility.
Spatial data
• Thespmatrix create command for creating standard-format spatial weighting matrices is now substantially faster.
Structural equation models
• Latent class model-comparison statistics
Survey data
• More powerful tables withsvy: tabulate
Survival analysis
• Interval-censored multiple-event Cox model
• Enhancements to survival graphs
  • sts graph has two new options,altrisktable andaltrisktable(), that provide an alternative at-risk table beneath the plot. This table reports the number at risk, the cumulative number lost, and the cumulative number of failures for each time point on thex axis or at user-specified time points. The new table also allows customization via suboptions. Several new suboptions, includingshow(),row(), andgrouptitle(), are available for further customization.
  • estat gofplot has new options for customizing goodness-of-fit plots. The newplot#opts() andbyplot#opts() options control the rendition of plots. Afterstmgintcox, the newevents() andsepevents options produce event-specific cumulative hazard functions; the newevent#opts() option controls the rendition of event-specific plots; and the newgraph#opts() andby#opts() options control the look of the#th graph.
  • stcurve has new options for customizing graphs of survivor and related functions. The newplot#opts() andatplot#opts() options control the rendition of plots. Afterstmgintcox, the newevents() andsepevents options produce event-specific functions; the newevent#opts() option controls the rendition of event-specific plots, the newgraph#opts() option controls the look of the#th graph, and the newbyopts() option controls how subgraphs are combined and labeled.
Time series
• SVAR models via instrumental variables
• Instrumental-variables local-projection IRFs
• Thevar command for fitting vector autoregressive (VAR) models now allows thevce(robust) option to estimate robust standard errors.
• Cumulative structural impulse–response functions (IRFs) are now computed byirf create afterivsvar andivlpirf. Cumulative structural IRFs sum individual IRFs and are useful for evaluating the total effect of a shock over time. You can graph these statistics withirf graph csirf and tabulate them withirf table csirf.

Download the updates

Make sure you have access to the new features released in updates. In Stata 19, type

. update all

in the Command window. Then type

. help whatsnew

to see a list of all the new features added since Stata 19 was released.

If you have StataNow, the above steps will also give you access to all StataNow features.




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