stata最大的弊端就是他的防盗版机制， stata发现你是盗版后不提醒你，也就是说你还在用stata，但stata已经知道你是盗版了， 会在计算结果中随机加入错误。   作为大部分用盗版的童鞋们， 这个你们怎么破？

你丫会读帖子吗? 我什么时候说我跑过几亿个数据?

受益匪浅，非常感谢！对你的理智、冷静与客观衷心地表示敬意！向你学习！

真的有这么严重哦？

有的人见到了一点东西就以为知道了全部，面对这种情况，你就一笑而过吧。
最近刚和美国做经济研究的学者交流过，人家用SAS的人也很多（包括数据处理和计量分析）。
不过对于Stata，R和Matlab，这几个软件都是数据先进内存，在内存处理计算。在大数据处理方面，SAS比其它软件是有优势的。不过对于大数据，尽管SAS不受内存制约，SAS处理起来也慢。对于Stata来说，我的电脑32G内存，确实经常处理几千万行的数据（比如美国所有上市公司1980年至今的daily stock price数据），对于上亿行的数据也处理过，对于几亿行（多于2亿）的没处理过。Stata最大支持1T的内存，也确实有朋友用512G内存的服务器运行Stata，不过一般高校经济金融专业不太可能具备这样的计算机。

在学R,觉得真的好难呀，不会编程不说，经常碰到错误提示，好麻烦呀，比起SPSS难多了。

{:3_41:}请问其他的统计软件有这种类似机制么？我一直用的SPSS盗版......

受教了

STATISTICAL SOFTWARE

Almost all serious statistical analysis is done in one of the following packages: R (S-PLUS), Matlab, SAS, SPSS and Stata. I have expertise in each of those packages but it does not mean that each of those packages is good for a specific type of analysis. In fact, for most advanced areas only 2-3 packages will be suitable, providing enough functionality or enough tools to implement this functionality easily. For example, a very important area of Markov Chain Monte Carlo is doable in R, Matlab and SAS only, unless you want to rely on convoluted macros written by random users on the web. The table at the end of this page compares the five packages in great detail.


R & MATLAB

R and Matlab are the richest systems by far. They contain an impressive collection of libraries, which is growing every day. Even if a desired specific model is not part of the standard functionality you can implement the model yourself, because R and Matlab are really programming languages with relatively simple syntaxes. As "languages" they allow you to express any idea. The question is whether you are a good writer or not. In terms of modern applied statistics tools, R libraries are somewhat richer than those of Matlab. Also R is free. On the flip side, Matlab has much better graphics, which you will not be ashamed to put in a paper or a presentation.


SPSS

On the other end of the spectrum is a package like SPSS. SPSS is quite narrow in its capabilities and allows you to do only about half of the mainstream statistics. It is quite useless for ambitious modeling and estimation procedures which are part of kernel smoothing, pattern recognition or signal processing. Nonetheless, SPSS is very popular among the practitioners because it does not require almost any training. All you have to do is hit several buttons and SPSS does all the calculations for you. In those cases when you need something standard, SPSS may have it implemented fully. The SPSS output will be quite detailed and visually pleasing. It will contain all the major tests and diagnostic tools associated with the method and will allow you to write an informative statistics section of your empirical analysis. In short, when the method is there, it is faster to run than a similar functionality in R or Matlab. So I use SPSS often for standard requests from my clients, like linear regression, ANOVA or principal components analysis. SPSS gives you the ability to program macros but that feature is quite inflexible.


SAS & STATA

Somewhere in-between R, Matlab and SPSS lie SAS and Stata. SAS is more extensive analytics than Stata. It is composed of dozens of procedures with massive, massive output, often covering more than ten pages. The idea of SAS is not to listen to you that much. It is like an old grandfather, whom you approach with a simple question but instead he tells you the story of his life. Many procedures contain three times more than what you need to know about that segment. So some time has to be spent on filtering in the relevant output. SAS procedures are invoked using simple scripts. Stata procedures can be invoked by clicking buttons in the menu or by running simple scripts. In the menu part, Stata resembles SPSS. Both SAS and Stata are programming languages, so they allow you to build analytics around standard procedures. Stata is somewhat more flexible than SAS. Still, in terms of programming flexibility, Stata and SAS do not come even close to R or Matlab. Selected strengths of SAS compared to all other packages: large data sets, speed, beautiful graphics, flexibility in formatting the output, time series procedures, counting processes. Selected strengths of Stata compared to all other packages: manipulation of survey data (stratified samples, clustering), robust estimation and tests, longitudinal data methods, multivariate time series.
                  

其实有excel就够了

请问由对于sas 和 matlab之间的比较吗？

看看。。。。。

R难上手但是灵活

还在学stata的路过……

写的非常好，受教了。

在国外读phd，用sas;准备以后涉足R~~~至于stata，着实没好感

非常同意您说的。本人是先用的matlab，没用过R。当时复制一篇关于IPO的论文，计量其实不重要，最重要的是data management，最起码要统计均值、中位数、分组之类的，用matlab做这种几百万、上千万observation的实证分析是非常不便的，指导研究的教授本身都用stata来做实证，我后来便也改用stata。matlab不知是否跟R较像，个人觉得matlab的确做计算很方便，但不是为做统计来设计的。至于矩阵，stata可以用mata，有时替代stata自带的回归分析等操作更为方便。
stata和sas相比，广泛程度应该不如后者，包括在业界（最简单的可以看看Wind数据库，有matlab sas r以及python的接口，但没有stata），一个实际问题就是更大规模数据的处理，因为stata是将数据先读入内存。但正如xingxf所言，提高机器的配置，stata在这方面的弱势并不会显得很大。实际应用中，尤其对于中国人，stata一个显见的好处在于其对于中文字符的支持，而sas是不行的。另，上学时一位牛校的计量大咖给我们讲计量前沿课程时，其实是推荐stata的，原因之一在于在服务器上可以方便的同时运行多个stata程序。。
如果只是做金融经济方面的实证分析，个人感觉stata综合素质还是很高的。最后举个例子，比如说投资，所谓建模可能更多时候是做统计，像Fama、French那样，而非真的有个像BS公式那样的数理模型，所以重在数据处理而非科学计算

也看得我很激动啊  我擦嘞

[handshake]

对比两年半之前的帖子，统计软件也有了一些进步。
这段时间之内，学习了MATLAB，SPSS的syntax，stata和R也比以前用的好多了。
总的体会：
1.MATLAB擅长数学分析，每个版本算法都会有多少的更新。而且可以做神经网络等分析，还可以模拟或者做GUI，虽然我没学好。。。
2.SPSS在被IBM收购之后也有很大的改进，加入了很多外部程序接口，可以运行R代码，也可以运行Python代码。我之前学了下SPSS的syntax，怎么讲，虽然总体感觉比较笨重，但有的时候又是不讲道理的简单，个中滋味妙不可言。
3.stata做统计、计量、社会学等分析非常好用，毕竟它是一个专门的“数据分析”软件，做数据清洗和计量很不错。至于有多少人用我不清楚，不过做统计分析时候，stata是我的首选。
stata可编程性也不错，尽管不如大多数人所讲的R、SAS这些软件的优势，但琢磨透了同样不难。归根结底，可能编程就是：函数、流程（条件语句，循环语句等）、元素和语法的结合；很多种编程语言本质上大同小异。（个人理解，勿喷）
4.R。我现在一般用R的ggplot包作图，仅与stata相比，说实话，R作图更胜一筹。尽管作图的命令脚本很复杂，但是stata做出来的是位图，像素低不好修改；R做出来的是矢量图，修改起来更方便。而且R的图形、色彩搭配更美观，感觉R的图形设计师肯定学过专业的设计知识，图形品味非常高。

循序渐进，由浅入深，不怕千招会，就怕一招精。我一个老同事，一份Excel＋VB套餐，20年一贯制，在他那个领域搞得风生水起。eviews算老掉牙了吧？但陈灯塔照样能玩出花样来。软件就是个工具，必须适用，够用即可，除非你以软件开发为专业。会观察，有idea才是王道