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Re: st: Stata 13 ships June 24


From   William Buchanan <william@williambuchanan.net>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: Stata 13 ships June 24
Date   Sun, 9 Jun 2013 15:16:37 -0700

Chuck Huber certainly was not wrong by any means when he told me that Stata 13 would have some amazing features...the anticipation from AERA till now was certainly worth it.

Sent from my iPhone

On Jun 9, 2013, at 15:03, "William Gould, StataCorp LP" <wgould@stata.com> wrote:

> Short explanation 
> -----------------
> 
> Stata 13 ships June 24.
> 
> You can order it starting now. 
> 
> Visit www.stata.com.
> 
> 
> 
> Longer explanation 
> ------------------
> 
> It is a tradition that Statalist members are the first to know when we
> have a new release.
> 
> Well, we have one.  I suspect the following new features will be of 
> the greatest interest:
> 
>   1.  Longer strings, even BLOBs (Binary Large OBjects)
> 
>       The maximum length of string variables increases from 244 to 
>       2-billion characters.  
> 
>       Yes, I'm referring to the string variables that we all have in
>       our .dta datasets.
> 
>       It works like this:  
> 
>           str1, str2, ..., str244    
>               (working just as previously)
> 
>           str245, ..., str2045      
>               (new and working just as you would expect)
> 
>           strL                       
>               (new, beyond 2,045, and working just like str#)
> 
>       strLs (pronounced sturls) can contain ascii or binary.
> 
>       strLs are coalesced, meaning the same copy of a strL is shared
>       across observations to save memory.
> 
>       You work with strLs the same as you work with str# variables.
>       All of Stata functions and commands work with them.  This
>       includes -substr()-, -generate-, -replace-, -by-, -sort-,
>       -tabulate-, etc.  The exception is that you cannot use a
>       strL variable as a key variable in a -merge-.
> 
>       Programmers:  strLs can be longer than local and global macros,
>       which have a maximum length of (only) 1,081,511 characters.
>       That means that all of Stata's string functions now work with
>       macros!
> 
> 
>   2.  Treatment-effects estimators.
> 
>       Treatment-effects estimators measure the causal effect of
>       treatment on an outcome in observational data.
> 
>       A new suite of features allows you to estimate average treatment
>       effects (ATE), average treatment effects on the treated (ATET),
>       and potential-outcome means (POMs).  Binary, multilevel, and
>       multivalued treatments are supported.  You can model outcomes
>       that are continuous, binary, count, or nonnegative.
> 
>       Different treatment-effects estimators are provided for
>       different situations.
> 
>       When you know the determinants of participation (but not the
>       determinants of outcome), inverse probability weights (IPW)
>       and propensity-score matching are provided.
> 
>       When you know the determinants of outcome (but not the
>       determinants of participation), regression adjustment and
>       covariate matching are provided.
> 
>       When you know the determinants of both, the doubly robust
>       methods augmented IPW and IPW with regression adjustment are
>       provided.  These methods are doubly robust because you need to
>       to be right about either the specification of outcome or the 
>       specification of participation, but not both. 
> 
>       Treatment effects are the subject of the all-new _Stata
>       Treatment-Effects Reference Manual_.
> 
> 
>   3.  Endogenous treatment-effect estimators
> 
>       As I said, treatment effects measure the causal effect of
>       treatment on outcome.  Sometimes we do not have conditional
>       independence, which is to say, unobserved variables affect both
>       treatment and outcome.
> 
>       The new endogenous treatment estimators address such cases. 
> 
>       -etregress- handles continuous outcome variables. 
> 
>       -etpoisson- handles count outcomes. 
> 
>       (-etregress- is an updated form of old command -treatreg-; -
>       etpoisson- is new.)
> 
> 
>   4.  Multilevel mixed-effects and generalized linear 
>       structural-equation modeling.
> 
>       Existing command -sem- fits linear SEMs.
> 
>       New command -gsem- joins -sem- and fits generalized SEMs. 
> 
>       Generalized SEMs is a term we have coined to mean
>       generalized linear response functions and to mean nested and
>       crossed effects, which can be used together or separately.
> 
>       Generalized linear response functions include linear regression,
>       naturally enough, and they include probit, logit, complementary
>       log-log, Poisson, negative binomial. multinomial logit, ordered
>       probit, ordered logit, and more.
> 
>       Nested and crossed effects means latent variables at different
>       levels of the data.  2 levels.  3 levels.  More levels.
> 
>       -gllamm- users:  There is a lot of overlap in the models that
>       -gllamm- and -gsem- can fit.  When there is overlap, -gsem- is
>       much faster.  
> 
> 
>   5.  More multilevel mixed-effects models.
> 
>       Stata already had multilevel mixed-effects linear, logistic, and
>       Poisson regression.
> 
>       Now we also have probit, complementary log-log, ordered
>       logistic, ordered probit, negative binomial, and generalized
>       linear models.
> 
>       And all the commands -- even the existing ones -- now allow
>       constraints on variance components and can provide robust and
>       cluster-robust standard errors.
> 
>       And the new commands are not only faster, they are bordering on
>       fast.
> 
>       Mixed-effects regression now has its own manual. 
> 
> 
>   6.  Forecasts.
> 
>       The new -forecast- command lets you combine results from
>       multiple Stata estimation commands and/or other sources to
>       produce dynamic or static forecasts and forecast intervals.
> 
>       Specify models.  Specify identities.  Obtain baseline forecast.
>       Specify alternative paths.  Obtain forecast.  That means
>       forecasts under alternative scenarios and ability to explore
>       impacts of differing policies.  Especially useful for
>       macroeconomic forecasts.
> 
> 
>   7.  Power and sample size
> 
>       Solve for power, sample size, minimum detectable effect, or
>       effect size.
> 
>       Comparisons of means (t tests), proportions, variances, correlations.
> 
>       Matched case-control studies, cohort studies, cross-sectional studies.
> 
>       Standard and customizable tables and graphs. 
> 
>       And its own manual. 
> 
> 
>   8.  New and extended random-effects panel-data estimators. 
> 
>       Ordered probit and ordered logistic join the existing
>       random-effects panel-data estimators linear regression,
>       interval-data regression, tobit, probit, logistic, complementary
>       log-log, and Poisson.
> 
>       Robust standard errors to relax distributional assumptions.
>       Cluster-robust standard errors for correlated data.
> 
> 
>   9.  Effects sizes. 
> 
>       Results the way behavioral scientists and especially
>       psychologists want to see them.
> 
>       Comparison of means:  Cohen's d, Hedges's g, Glass's Delta,
>       point/biserial correlation.  Estimated from data or from
>       published summary statistics.
> 
>       Variances explained by regression and ANOVA: Eta-squared and
>       partial eta-squared, omega-squared and partial omega-squared.
>       Partial statistics estimated from data.  Overall statistics from
>       data or from published summary statistics.
> 
> 
>  10.  Project Manager.
> 
>       Organize any kind of file (do-files, ado-files, datasets, raw
>       files, etc.) into hierarchical list for quick access. 
>       Manage hundreds, even thousands, of files per project. 
> 
>       Manage multiple projects. 
> 
>       Create groups in project to categorize files. 
> 
>       Click to open datasets, display saved graphs, open do-files in the 
>       Do-file Editor. 
> 
>       Rename file.  Filter on filenames. Search for file using keywords. 
> 
>       You have to try it to appreciate it, but in the meantime, you
>       can find pictures at www.stata.com.
> 
> 
>   11. Java plugins.
> 
>       Call Java methods directly from Stata.  Interact with Stata's
>       datasets, matrices, macros, etc.  Take advantage of existing
>       Java libraries, or write your own code.
> 
> 
>   12. And more 
> 
>       I should mention the improved help-file searching, and that
>       Stata now supports secure HTTP and FTP, and fast PDF manual
>       navigation, and ordered probit with Heckman-style sample
>       selection, and the new way of estimating ML models without
>       writing an evaluator program, and the new fractional-polynomial
>       prefix command, and that quantile regression can now produce
>       robust estimates of standard errors, and that factor variables
>       now support value labels for labeling output, and the new way
>       to import data from Haver Analytics, and automatic
>       business-calendar creation, and the new import commands that
>       make reading data really easy, and how you can create Word and
>       Excel files from Stata, and solve arbitrary nonlinear systems,
>       and a lot of other things.
> 
> 
> I could go on, but instead I'll mention that we have finally implemented the
> feature that is the most requested at user meetings around the world:
> 
> You can now type -cls- to clear the Results Window. 
> 
> -- Bill
> wgould@stata.com
> 
> 
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