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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 15:35:45 -0700

And just to clarify, he told me only that it would have some amazing additions/features and did not tell me what any of the features would potentially be.  Either way there is a lot of cool stuff in the new release.

Sent from my iPhone

On Jun 9, 2013, at 15:16, William Buchanan <william@williambuchanan.net> wrote:

> 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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