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Re: st: Stata 13 ships June 24
From
William Buchanan <[email protected]>
To
"[email protected]" <[email protected]>
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 <[email protected]> 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" <[email protected]> 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
>> [email protected]
>>
>>
>> *
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>
> *
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*
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