New — Bayesian analysis commands / Treatment-effect analysis / IRT (Item Response Theory) Analysis / Support for Unicode / Stata in new languages / New time series commands / and much more…
End User License Agreement
Stata 14 is a complete, integrated statistical package that provides everything you need for data analysis, data management, and graphics. Stata is not sold in modules, which means you get everything you need in one package. And, you can choose a perpetual licence, with nothing more to buy ever. Annual licences are also available.
All of the following flavours of Stata have the same complete set of commands and features and manuals included as PDF documentation within Stata.
Stata/MP: The fastest version of Stata (for dual-core and multicore/multiprocessor computers);
Stata/MP is the fastest and largest version of Stata. Most computers purchased since mid 2006 can take advantage of the advanced multiprocessing of Stata/MP. This includes the Intel Core™ 2 Duo, i3, i5, i7, and the AMD X2 dual-core chips. On dual-core chips, Stata/MP runs 40% faster overall and 72% faster where it matters - on the time-consuming estimation commands. With more than two cores or processors, Stata/MP is even faster.
Stata/MP is a version of Stata/SE that runs on multiprocessor and multicore computers. Stata/MP provides the most extensive support for multiprocessor computers and multicore computers of any statistics and data-management package.
The exciting thing about Stata/MP, and the only difference between Stata/MP and Stata/SE, is that Stata/MP runs faster—much faster. Stata/MP lets you analyse data in one-half to two-thirds of the time compared with Stata/SE on inexpensive dual-core desktops and laptops and in one-quarter to one-half the time on quad-core desktops. Stata/MP runs even faster on multiprocessor servers. Stata/MP supports up to 64 processors/cores.
In a perfect world, software would run twice as fast on two cores, four times as fast on four cores, eight times as fast on eight cores, and so on. Across all commands, Stata/MP runs 1.6 times faster on two cores, 2.1 times faster on four cores, and 2.7 times faster on eight cores. These values are median speed improvements. Half the commands run even faster.
On the other side of the distribution, a few commands do not run faster, often because they are inherently sequential, such as time-series commands.
Stata worked hard to make sure that the performance gains for commands that take longer to run would be greater. Across all estimation commands, Stata/MP runs 1.8 times faster on dual-core computers, 2.8 times faster on quad-core computers, and 4.1 times faster on computers with eight cores.
Stata/MP is 100% compatible other versions of with Stata. Analyses do not have to be reformulated or modified in any way to obtain Stata/MP’s speed improvements.
Stata/MP is available for the following operating systems:
Windows (32- and 64-bit processors);
Mac OS X (64-bit Intel processors);
Linux (32- and 64-bit processors);
Solaris (64-bit SPARC and x86-64).
To run Stata/MP, you can use a desktop computer with a dual-core or quad-core processor, or you can use a server with multiple processors. Whether a computer has separate processors or one processor with multiple cores makes no difference. More processors or cores makes Stata/MP run faster.
For more advice on purchasing/upgrading to Stata/MP or for hardware queries, please contact our sales team.
Stata SE performs in the same way as Stata/MP, allowing for the same number of variables and observations and the only difference is that it is not designed for parallel processing.
In addition, Stata/SE, Stata/IC and Small Stata differ only in the dataset size that each can analyse Stata/SE and Stata/MP can fit models with more independent variables than Stata/IC (up to 10,998).
Stata/IC allows datasets with as many as 2,047 variables. The maximum number of observations is 2.14 billion. Stata/IC can have at most 798 right-hand-side variables in a model.
Small Stata is limited to analysing datasets with a maximum of 99 variables and 1,200 observations. Small Stata can have at most 99 right-hand-side variables in a model.
Comparison of features
Stata/MP
Stata/SE
Stata/IC
Small Stata
Max. no. of variables
32,767
32,767
2,047
99
Max. no. of right-hand variables
10,998
10,998
798
99
Max. no. of observations
20 billion*
2.14 billion
2.14 billion
1,200
64-bit compatible?
Yes
Yes
Yes
Yes
Allows parallel processing?
Yes
No
No
No
Platforms
Windows, Mac OS X (64-bit Intel), Unix
Minimum memory required
2 GB
1 GB
512 MB
512 MB
Minimum disk space required
900 MB
900 MB
900 MB
900 MB
* The Maximum number of observations is limited only by the amount of available RAM on your system.
Whether you're a student or a seasoned research professional, a range of Stata packages are available and designed to suit all needs.
All of the following flavours of Stata have the same, complete set of commands and features and include PDF documentation:
Stata/MP: The fastest version of Stata (for dual- and multicore/multiprocessor computers)
Stata/SE: Stata for large datasets
Stata/IC: Stata for moderate-sized datasets
Small Stata: A version of Stata that handles small datasets (for educational purchases only)
What Stata is right for me?
The summary above shows the Stata packages available.
Stata/MP is the fastest and largest version of Stata. Most computers purchased after mid-2006 can take advantage of the advanced multiprocessing capabilities of Stata/MP.
Stata/MP, Stata/SE, and Stata/IC all run on any machine, but Stata/MP runs faster. You can buy a Stata/MP license for up to the number of cores on your machine (the most is 64). For example, if your machine has eight cores, you can buy a Stata/MP license for either eight cores (Stata/MP8), four cores (Stata/MP4), or two cores (Stata/MP2).
Stata/MP can also analyse more data than any other flavour of Stata. Stata/MP can analyse 10 to 20 billion observations given the current largest computers, and is ready to analyse up to 281 trillion observations once computer hardware catches up.
Stata/SE, Stata/IC, and Small Stata differ only in the dataset size that each can analyse. Stata/SE and Stata/MP can fit models with more independent variables than Stata/IC (up to 10,998). Stata/SE can analyse up to 2 billion observations.
Stata/IC allows datasets with as many as 2,047 variables and 2 billion observations. Stata/IC can have at most 798 right-hand-side variables in a model.
Small Stata is limited to analysing datasets with a maximum of 99* variables and 1,200* observations. Small Stata can have at most 98 right-hand-side variables in a model.
Note: The number of variables and observations allowed by Small Stata includes the additional variables or observations generated during statistical computations.
FUNCIONALIDADES in Stata 14
Stata 14 has 102 new features and is one of the biggest new releases of Stata and offers new research capabilities for users in a variety of fields such as: economics, health researchers, epidemiologists, sociologists, psychologists, education researchers, political scientists, and econometricians.
Bayesian analysis commands
The introduction of Bayesian analysis commands (univariate and multivariate linear models, univariate GLM, univariate and generalized nonlinear models, etc.) supported by an all new Stata Bayesian Analysis reference manual.
Stata 14 includes 12 built-in likelihood models and 22 built-in prior distributions among other helpful features. More
Extended models of treatment effects
Treatment-effect analysis is now available for a much broader class of models. Endogenous treatment-effect estimation is now available for continuous, binary, count, and fractional outcomes.
Treatment effects can now also be estimated from observational survival data. More
IRT (item response theory) analysis
Stata 14 now supports IRT models for binary items (1-3 PL), categorical items (nominal response), ordinal items (graded response, rating scale and partial credit) and any combination of those models. More
Stata in new languages
Stata’s user interface is now available in Spanish and Japanese. More
More useful new features added in Stata 14 are:
You can fit a variety of multilevel survival models such as exponential and Weibull mixed-effects models. More
You can perform small-sample inference in linear mixed models using several denominator degrees-of-freedom methods, including the Kenward-Roger method. More
You can conduct the Satorra-Bentler adjusted model test for SEMs with data that are not normally distributed. More
You can estimate models for rates, proportions, and other fractional responses using beta regression and fractional regression models.
You can estimate Poisson models with censored dependent variables.
Stata/MP now allows more than 2.1 billion observations – up to 20 billion observations given the current largest computer, and is ready for more once computer hardware catches up. More
churdle to estimate linear and exponential hurdle models
betareg and fracreg for fractional responses, proportions, rates, etc.
cpoisson to estimate censored Poisson models
ztest and ztesti commands to compute z-statistics
Postestimation Selector that greatly simplifies postestimation analysis
Nearly all estimation commands in Stata now support factor variables
A multitude of improvements to margins, such as the ability to make multiple predictions at a time and having the default predictions reflect the best choice for marginal analysis
Several new utilities to help you better manage graphs
New Quick start section of the manuals
New Stata Functions Reference Manual
Programming your thing...? You'll be interested in these new features in Stata 14.
Stata now uses the 64-bit Mersenne twister as its default random-number generator
New statistical, random-number distribution, and string functions
All new functions added to Stata are also available in Mata
There are many video tutorials in using Stata. Below you will find the most recent additions that relate to Stata 14, as well as a list of all other resources currently available.
Quick tips
Converting string variables to numeric
Partial dataset
How to download and install Stata for Windows
Tour of Stata 14
Tour of the Stata 14 interface
PDF documentation in Stata 14
Bayesian analysis in Stata
Censored Poisson regression in Stata
Endogenous treatment effects in Stata
Graphical user interface for Bayesian analysis in Stata
IRT (item response theory) models in Stata
Japanese and Spanish interface in Stata 14
Markov-switching models in Stata
Multilevel models for survey data in Stata
Multilevel survival analysis in Stata
New power and sample-size features in Stata
Panel-data survival models in Stata
Postestimation Selector in Stata
Regression models for fractional data in Stata
Satorra–Bentler adjustments for SEM
Small-sample inference for mixed-effects models in Stata
Survey data support for SEM in Stata
Survival models for SEM in Stata
Treatment effects for survival models in Stata
Unicode in Stata
Below you will find a list of all video tutorial resources available. The links will take you to YouTube.
All versions of Stata run on dual-core, multi-core and multi-processor computers.
Stata for Windows
Windows 10 *
Windows 8 *
Windows 7 *
Windows Vista *
Windows Server 2012 *
Windows Server 2008 *
Windows Server 2003 *
* 64-bit and 32-bit Windows varieties for x86-64 and x86 processors made by Intel® and AMD.
Stata for Mac
Stata for Mac requires 64-bit Intel® processors (Core™2 Duo or better) running OS X 10.7 or newer
Stata for Unix
Linux: Any 64-bit (x86-64 or compatible) or 32-bit (x86 or compatible) running Linux.
Hardware requirements
Minimum of 512 MB of RAM
Minimum of 900 MB of disk space
Stata for Unix requires a video card that can display thousands of colours or more (16-bit or 24-bit colour)
Clique para informações sobre preços
Stata 14 Documentation
Every installation of Stata includes all the documentation in PDF format. Stata’s documentation consists of over 12,000 pages detailing each feature in Stata including the methods and formulas and fully worked examples. You can transition seamlessly across entries using the links within each entry.
The Stata 14 documentation is copyright of StataCorp LP, College Station TX, USA, and is used with permission of StataCorp LP.
ESTUDANTES may purchase Stata/MP, Stata/SE, Stata/IC and Small Stata at a discounted price through the Stata GradPlan programme. For more information about available licence types, click here.
We are pleased to be a bronze partner at the Informatics for Health 2017 conference to be held in Manchester between 24-26 April 2017.
Stop by Stand 25 to learn more abo...
Date: Thursday, 7 & Friday, 8 September 2017
Location: Cass Business School, London EC1Y 8TZ.
The London Stata Users Group meeting is a two-day international conference...
Date: Friday, 15 September 2017
Location: Economics Faculty – Porto University.
The Portuguese Stata Users Group meeting is a one-day international conference to discu...
2016 saw us celebrate twenty-five years of distributing and supporting Stata to users within the UK & Ireland. We are very proud of our close working relationship with S...
Financial Econometrics Using Stata by Simona Boffelli and Giovanni Urga provides an excellent introduction to time-series analysis and how to do it in Stata for financial...
This course will provide participants with the essential tools, both theoretical and applied, for a proper use of modern micro-econometric methods for policy evaluation and causal counterfactual modelling under the assumption of “selection on observables”.
This 2-day course provides a review of and a practical guide to several major econometric methodologies frequently used to model the stylised facts of the financial time series via ARMA models, univariate and multivariate GARCH models, risk management analysis and contagion. Demonstration of the alternative techniques will be illustrated using Stata. Practical sessions within the course involve interest rate data, asset prices and forex time series.
The course is delivered by Prof. Giovanni Urga, an author of Financial Econometrics using Stata - Boffelli, S and Urga, G (2016), Stata Press: TX. The course is based on the book and all attendees will receive a complimentary copy.
Our 2017 Econometrics Summer School, Cambridge will take place on 15-22 July 2017 and will be held at Clare College, University of Cambridge. The School comprises 3x 2.5-day econometrics short courses delivered by leading Econometricians from the University of Cambridge: Prof. Andrew Harvey, Prof. Sean Holly and Dr. Melvyn Weeks.
The three courses comprising the School are - Time Series Analysis & Modelling / Macroeconomic Modelling & Forecasting / Microeconometrics
This 1-day course provides a complete introduction to Stata and is ideal for the new or beginner level user who want to have a head start and learn how to use Stata efficiently.
This course provides a review of and a practical guide to several major econometric methodologies to modelling the stylised facts of the energy prices and demand time series, via regression and cointegration analysis, univariate and multivariate GARCH models.
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Call on +44(0) 20 8697 3377, email on info@timberlake.co.uk or contact us on the form below.