stata使用手册
Stata 使用手册说明书
1Read this—it will helpContents1.1Getting Started with Stata1.2The User’s Guide and the Reference manuals1.2.1PDF manuals1.2.1.1Video example1.2.2Example datasets1.2.2.1Video example1.2.3Cross-referencing1.2.4The index1.2.5The subject table of contents1.2.6Typography1.2.7Vignette1.3What’s new1.4References12[U]1Read this—it will helpThe Stata Documentation consists of the following manuals:[GSM]Getting Started with Stata for Mac[GSU]Getting Started with Stata for Unix[GSW]Getting Started with Stata for Windows[U]Stata User’s Guide[R]Stata Base Reference Manual[ADAPT]Stata Adaptive Designs:Group Sequential Trials Reference Manual[BAYES]Stata Bayesian Analysis Reference Manual[BMA]Stata Bayesian Model Averaging Reference Manual[CAUSAL]Stata Causal Inference and Treatment-Effects Estimation Reference Manual[CM]Stata Choice Models Reference Manual[D]Stata Data Management Reference Manual[DSGE]Stata Dynamic Stochastic General Equilibrium Models Reference Manual[ERM]Stata Extended Regression Models Reference Manual[FMM]Stata Finite Mixture Models Reference Manual[FN]Stata Functions Reference Manual[G]Stata Graphics Reference Manual[IRT]Stata Item Response Theory Reference Manual[LASSO]Stata Lasso Reference Manual[XT]Stata Longitudinal-Data/Panel-Data Reference Manual[META]Stata Meta-Analysis Reference Manual[ME]Stata Multilevel Mixed-Effects Reference Manual[MI]Stata Multiple-Imputation Reference Manual[MV]Stata Multivariate Statistics Reference Manual[PSS]Stata Power,Precision,and Sample-Size Reference Manual[P]Stata Programming Reference Manual[RPT]Stata Reporting Reference Manual[SP]Stata Spatial Autoregressive Models Reference Manual[SEM]Stata Structural Equation Modeling Reference Manual[SVY]Stata Survey Data Reference Manual[ST]Stata Survival Analysis Reference Manual[TABLES]Stata Customizable Tables and Collected Results Reference Manual[TS]Stata Time-Series Reference Manual[I]Stata Index[M]Mata Reference ManualIn addition,installation instructions may be found in the Installation Guide.[U]1Read this—it will help3 1.1Getting Started with StataThere are three Getting Started manuals:[GSM]Getting Started with Stata for Mac[GSU]Getting Started with Stata for Unix[GSW]Getting Started with Stata for Windows1.Learn how to use Stata—read the Getting Started(GSM,GSU,or GSW)manual.2.Now turn to the other manuals;see[U]1.2The User’s Guide and the Reference manuals.1.2The User’s Guide and the Reference manualsThe User’s Guide is divided into three sections:Stata basics,Elements of Stata,and Advice.The table of contents lists the chapters within each of these sections.Click on the chapter titles to see the detailed contents of each chapter.The Guide is full of a lot of useful information about Stata;we recommend that you read it.If you only have time,however,to read one or two chapters,then read[U]11Language syntax and [U]12Data.The other manuals are the Reference manuals.The Stata Reference manuals are each arranged like an encyclopedia—alphabetically.Look at the Base Reference Manual.Look under the name ofa command.If you do notfind the command,look in the subject index in[I]Stata Index.A fewcommands are so closely related that they are documented together,such as ranksum and median, which are both documented in[R]ranksum.Not all the entries in the Base Reference Manual are Stata commands;some contain technical information,such as[R]Maximize,which details Stata’s iterative maximization process,or[R]Error messages,which provides information on error messages and return codes.Like an encyclopedia,the Reference manuals are not designed to be read from cover to cover.When you want to know what a command does,complete with all the details,qualifications,and pitfalls,or when a command produces an unexpected result,read its description.Each entry is written at the level of the command.The descriptions assume that you have little knowledge of Stata’s features when they are explaining simple commands,such as those for using and saving data.For more complicated commands,they assume that you have afirm grasp of Stata’s other features.If a Stata command is not in the Base Reference Manual,you canfind it in one of the other Reference manuals.The titles of the manuals indicate the types of commands that they contain.The Programming Reference Manual,however,contains commands not only for programming Stata but also for manipulating matrices(not to be confused with the matrix programming language described in the Mata Reference Manual).1.2.1PDF manualsEvery copy of Stata comes with Stata’s complete PDF documentation.The PDF documentation may be accessed from within Stata by selecting Help>PDF documentation.Even more convenient,every helpfile in Stata links to the equivalent manual entry.If you are reading help regress,simply click on(View complete PDF manual entry)below the title of the helpfile to go directly to the[R]regress manual entry.We provide some tips for viewing Stata’s PDF documentation at https:///support/ faqs/resources/pdf-documentation-tips/.4[U]1Read this—it will help1.2.1.1Video examplePDF documentation in Stata1.2.2Example datasetsVarious examples in this manual use what is referred to as the automobile dataset,auto.dta.We have created a dataset on the prices,mileages,weights,and other characteristics of74automobiles and have saved it in afile called auto.dta.(These data originally came from the April1979issue of Consumer Reports and from the United States Government EPA statistics on fuel consumption;they were compiled and published by Chambers et al.[1983].)In our examples,you will often see us type.use https:///data/r18/autoWe include the auto.dtafile with Stata.If you want to use it from your own computer rather than via the Internet,you can type.sysuse autoSee[D]sysuse.You can also access auto.dta by selecting File>Example datasets...,clicking on Example datasets installed with Stata,and clicking on use beside the auto.dtafilename.There are many other example datasets that ship with Stata or are available over the web.Here isa partial list of the example datasets included with Stata:auto.dta1978automobile databplong.dta Fictional blood-pressure data,long formbpwide.dta Fictional blood-pressure data,wide formcancer.dta Patient survival in drug trialcensus.dta1980Census data by statecitytemp.dta U.S.city temperature dataeduc99gdp.dta Education and gross domestic productgnp96.dta U.S.gross national product,1967–2002lifeexp.dta1998life expectancynetwork1.dta Fictional network diagram datanlsw88.dta1988U.S.National Longitudinal Survey of Young Women(NLSW),extractpop2000.dta2000U.S.Census population,extractsandstone.dta Subsea elevation of Lamont sandstone in an area of Ohiosp500.dta S&P500historic datasurface.dta NOAA sea surface temperaturetsline1.dta Simulated time-series datauslifeexp.dta U.S.life expectancy,1900–1999voter.dta1992U.S.presidential voter dataAll of these datasets may be used or described from the Example datasets...menu listing.Even more example datasets,including most of the datasets used in the reference manuals,are available at the Stata Press website(https:///data/).You can download the datasets with your browser,or you can use them directly from the Stata command line:.use https:///data/r18/nlswork[U]1Read this—it will help5An alternative to the use command for these example datasets is webuse.For example,typing .webuse nlsworkis equivalent to the above use command.For more information,see[D]webuse.1.2.2.1Video exampleExample datasets included with Stata1.2.3Cross-referencingThe Getting Started manual,the User’s Guide,and the Reference manuals cross-reference each other.[R]regress[D]reshape[XT]xtregThefirst is a reference to the regress entry in the Base Reference Manual,the second is a reference to the reshape entry in the Data Management Reference Manual,and the third is a reference to the xtreg entry in the Longitudinal-Data/Panel-Data Reference Manual.[GSW]B Advanced Stata usage[GSM]B Advanced Stata usage[GSU]B Advanced Stata usageare instructions to see the appropriate section of the Getting Started with Stata for Windows,Getting Started with Stata for Mac,or Getting Started with Stata for Unix manual.1.2.4The indexThe Stata Index contains a combined index for all the manuals.Tofind information and commands quickly,you can use Stata’s search command;see[R]search.At the Stata command prompt,type search geometric mean.search searches Stata’s keyword database and the Internet tofind more commands and extensions for Stata written by Stata users.1.2.5The subject table of contentsA subject table of contents for the User’s Guide and all the Reference manuals is located in theStata Index.This subject table of contents may also be accessed by clicking on Contents in the PDF bookmarks.1.2.6TypographyWe mix the ordinary typeface that you are reading now with a typewriter-style typeface that looks like this.When something is printed in the typewriter-style typeface,it means that something is a command or an option—it is something that Stata understands and something that you might actually type into your computer.Differences in typeface are important.If a sentence reads,“You could list the result...”,it is just an English sentence—you could list the result,but the sentence provides no clue as to how you might actually do that.On the other hand,if the sentence reads,“You could list the result...”,it is telling you much more—you could list the result,and you could do that by using the list command.6[U]1Read this—it will helpWe will occasionally lapse into periods of inordinate cuteness and write,“We describe d the data and then list ed the data.”You get the idea.describe and list are Stata commands.We purposely began the previous sentence with a lowercase letter.Because describe is a Stata command,it must be typed in lowercase letters.The ordinary rules of capitalization are temporarily suspended in favor of preciseness.We also mix in words printed in italic type,such as“To perform the rank-sum test,type ranksum varname,by(groupvar)”.Italicized words are not supposed to be typed;instead,you are to substitute another word for them.We would also like users to note our rule for punctuation of quotes.We follow a rule that is often used in mathematics books and British literature.The punctuation mark at the end of the quote is included in the quote only if it is a part of the quote.For instance,the pleased Stata user said she thought that Stata was a“very powerful program”.Another user simply said,“I love Stata.”In this manual,however,there is little dialogue,and we follow this rule to precisely clarify what you are to type,as in,type“cd c:”.The period is outside the quotation mark because you should not type the period.If we had wanted you to type the period,we would have included two periods at the end of the sentence:one inside the quotation and one outside,as in,type“the orthogonal polynomial operator,p.”.We have tried not to violate the other rules of English.If youfind such violations,they were unintentional and resulted from our own ignorance or carelessness.We would appreciate hearing about them.We have heard from Nicholas J.Cox of the Department of Geography at Durham University,UK, and express our appreciation.His efforts have gone far beyond dropping us a note,and there is no way with words that we can fully express our gratitude.1.2.7VignetteIf you look,for example,at the entry[R]brier,you will see a brief biographical vignette of Glenn Wilson Brier(1913–1998),who did pioneering work on the measures described in that entry.A few such vignettes were added without fanfare in the Stata8manuals,just for interest,and many more were added in Stata9,and even more have been added in each subsequent release.A vignette could often appropriately go in several entries.For example,George E.P.Box deserves to be mentioned in entries other than[TS]arima,such as[R]boxcox.However,to save space,each vignette is given once only,and an index of all vignettes is given in the Stata Index.Most of the vignettes were written by Nicholas J.Cox,Durham University,and were compiled using a wide range of reference books,articles in the literature,Internet sources,and information from individuals.Especially useful were the dictionaries of Upton and Cook(2014)and Everitt and Skrondal(2010)and the compilations of statistical biographies edited by Heyde and Seneta(2001) and Johnson and Kotz(1997).Of these,only thefirst provides information on people living at the time of publication.1.3What’s newThere are a lot of new features in Stata18.For a thorough overview of the most important new features,visithttps:///new-in-stata/[U]1Read this—it will help7 For a brief overview of all the new features that were added with the release of Stata18,in Stata type.help whatsnew17to18Stata is continually being updated.For a list of new features that have been added since the release of Stata18,in Stata type.help whatsnew181.4ReferencesChambers,J.M.,W.S.Cleveland,B.Kleiner,and P.A.Tukey.1983.Graphical Methods for Data Analysis.Belmont, CA:Wadsworth.Everitt, B.S.,and A.Skrondal.2010.The Cambridge Dictionary of Statistics.4th ed.Cambridge:Cambridge University Press.Gould,W.W.2014.Putting the Stata Manuals on your iPad.The Stata Blog:Not Elsewhere Classified./2014/10/28/putting-the-stata-manuals-on-your-ipad/.Heyde,C.C.,and E.Seneta,ed.2001.Statisticians of the Centuries.New York:Springer.Johnson,N.L.,and S.Kotz,ed.1997.Leading Personalities in Statistical Sciences:From the Seventeenth Century to the Present.New York:Wiley.Pinzon,E.,ed.2015.Thirty Years with Stata:A Retrospective.College Station,TX:Stata Press.Upton,G.J.G.,and I.T.Cook.2014.A Dictionary of Statistics.3rd ed.Oxford:Oxford University Press.Stata,Stata Press,and Mata are registered trademarks of StataCorp LLC.Stata andStata Press are registered trademarks with the World Intellectual Property Organization®of the United Nations.Other brand and product names are registered trademarks ortrademarks of their respective companies.Copyright c 1985–2023StataCorp LLC,College Station,TX,USA.All rights reserved.。
STATA基本操作入门
8.相关系数
• 如果要显示PL,PF两个变量的相关系数 • 方法：pwcorr pl pf
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• 方法：pwcorr pl pf pk
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8.1 相关系数
• 如果要显示PL,PF,PK三个变量之间的相关 系数，并显示显著性水平
• 保存该图：输入graph save scatter2
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9.6 图像合并展示
• 将线性拟合和二次拟合这两个图像在一起 展示
• 方法:输入graph combine scatter1.gph scatter2.gph
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此课件下载可自行编辑修改，此课件供参考！ 部分内容来源于网络，如有侵权请与我联系删除！感谢你的观看！
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9.3 画图：散点图
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9.3.1 散点图改进
• 定义新变量值n来表示第n个观测值： • 方法：gen n=_n （_n表示第n个观测值） • 使散点图显示对应的观测值： • 方法：scatter tc q,mlabel(n) mlabpos(6)
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• 展示变量q的样本容量，平均值，标准差， 最小值，最大值
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6.2查看变量的统计特征
• 如果要查看满足q≥10000的子样本的统计指 标。方法：输入summarize q if q >=10000
• 或者su q if q >=10000
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6.3 查看变量的统计特征
Properties: 性质窗口，
显示当前数
据文件和变 量的性质
stata入门教程
Stata 快速入门1、Stata的窗口•在最上方有一排菜单，即“File Edit Data Graphics Statistics User Window Help”。
•左上“Review”（历史窗口）：此窗口记录着自启动Stata以来执行过的命令。
•右上“Variables”（变量窗口）：此窗口记录着目前Stata内存中的所有变量。
•正上方“Results”（结果窗口）：此窗口显示执行Stata命令后的输出结果。
•正下方“Command”（命令窗口）：在此窗口输入想要执行的Stata命令。
2、将数据导入Stata•打开Stata软件后，点击Data Editor(Edit)图标（也可以点击菜单“Window”→“Data Editor”），即可打开一个类似Excel的空白表格。
•用Excel打开文件“nerlove.xls”，复制文件中的所有数据，并粘贴到Data Editor 中。
•导入数据的另一方法是，点击菜单“File”→“Import”，然后导入各种格式的数据。
但这种方法有时不如直接从Excel表中粘贴数据来得方便直观。
3、变量窗口•关闭Data Editor后，即会看到右上方的“Variables”窗口出现了5个变量：•分别为tc（total cost，总成本），q（total output, 总产量），pl（price of labor，小时工资率），pf（price of fuel，燃料价格），与pk（user cost of capital，资本的租赁价格。
4、存为dta数据文件•此时，可以点击Save图标（也可以点击菜单“File”→“Save”），将数据存为Stata格式的文件（扩展名为dta），比如nerlove.dta。
•以后就可以用Stata直接打开这个数据集了（不需要再从Excel表中粘贴过来）。
5、打开dta数据文件打开的方式有三种：1.点击Open图标（也可以点击菜单“File”→“Open”），然后寻找要打开的dta 文件的位置。
stata使用手册
STATA基本入门前言STATA是一个十分好用而且简单的统计软件包，透过轻松的数据输入方式，而且简单的指令，即可执行一般在计量经济学上常用的计量模型。
除了计量模型外，STATA的软件包中也可执行统计学中的估计和检定，甚至是多变量分析中的各项分析工具。
因此，STATA可以说是一个相当强而有力的统计软件。
一、安装STATA所须的内存容量不大，只有4.03MB。
此外，安装也相当简单，只要在〝SETUP〞上点两下，安装完成后再分别输入”Sn”、”Code”和”Key”即可开始使用。
但是安装过程中有一点必须注意的是，如下图所示，有”Intercooled”和”Small”两个选项。
一般而言，为了方便日后要设定较大的内存容量来处理大笔的资料，通常选择以”Intercooled”进行安装。
以此項進行安裝二、窗口介绍安装完成后，点选桌面上STATA的图标，窗口画面如下图所示。
为了使画面美观，我们可以将画面拉到自己喜欢的地方，如下图所示。
为了保存这个窗口画面，我们必须点选工具列上的”Prefs”下的”Save Windowing Preferences”。
如此一来，以后开启STATA 时都会以此窗口画面呈现。
接下来，我们依序介绍四个窗口的功用：左上─Review：此一窗口用于记录在开启STATA后所执行过的所有指令。
因此，若欲使用重复的指令时，只要在该指令上点选两下即可执行相同的指令；若欲使用类似的指令时，在该指令上点一下，该指令即会出现在窗口”Stata Command”上，再进行修改即可。
此外，STATA还可以将执行过的指令储存下来，存在一个do-file内，下次即可再执行相同的指令。
左下─Variables：此一窗口用于呈现某笔数据中的所有变量。
换言之，当数据中的变量都有其名称时，变量名称将会出现在此一窗口中。
只要数据有读进STATA中，变量名称就会出现。
它的优点是(1)确认数据输入无误；(2)只要在某变量上点选两下，该变量即会出现在窗口”Stata Command”上。
stata17 中文操作手册
stata17 中文操作手册【实用版】目录1.Stata 17 简介2.Stata 17 中文操作手册的主要内容3.如何使用 Stata 17 进行数据分析4.Stata 17 的新特性和功能5.总结正文Stata 17 是一款专业的数据分析软件，广泛应用于社会科学、生物统计学、医学统计学等领域。
Stata 17 中文操作手册为使用者提供了详细的操作指南，帮助用户更好地掌握软件的使用方法。
一、Stata 17 简介Stata 17 是由美国 Stata 公司开发的一款数据分析软件。
它具有强大的数据处理、分析和绘图功能，以及丰富的命令和语法，可以满足各种数据分析需求。
Stata 17 对中文的支持十分友好，用户可以方便地使用中文进行数据处理和分析。
二、Stata 17 中文操作手册的主要内容Stata 17 中文操作手册主要包括以下几个方面的内容：1.软件安装与激活：手册中详细介绍了 Stata 17 的安装过程和激活方法，以确保用户可以正确地使用软件。
2.数据的输入与处理：手册中讲解了如何使用 Stata 17 输入和处理数据，包括数据的导入、转换、合并、筛选等操作。
3.统计分析：手册中涵盖了各种统计分析方法，包括描述性统计、t 检验、方差分析、回归分析等。
4.绘图：Stata 17 具有强大的绘图功能，手册中详细介绍了如何使用 Stata 17 进行数据可视化，包括绘制柱状图、饼图、散点图等。
5.编程与定制：手册中讲解了如何使用 Stata 17 进行编程，用户可以根据自己的需求编写自定义命令和语法。
三、如何使用 Stata 17 进行数据分析使用 Stata 17 进行数据分析的步骤如下：1.安装和激活软件：按照手册中的指导进行软件安装和激活。
2.打开数据文件：在 Stata 17 中打开需要分析的数据文件。
3.数据清洗：使用 Stata 17 提供的命令和语法对数据进行清洗，包括数据的导入、转换、合并、筛选等操作。
STATA统计分析软件使用教程
STATA统计分析软件使用教程引言STATA统计分析软件是一款功能强大、使用广泛的统计分析软件，广泛应用于经济学、社会学、医学和其他社会科学领域的研究中。
本教程将介绍STATA的基本操作和常用功能，并提供实例演示，帮助读者快速上手使用。
第一章：STATA入门1.1 安装与启动首先，下载并安装STATA软件。
完成安装后，点击软件图标启动STATA。
1.2 界面介绍STATA的界面分为主窗口、命令窗口和结果窗口。
主窗口用于数据显示，命令窗口用于输入分析命令，结果窗口用于显示分析结果。
1.3 数据导入与保存使用命令`use filename`导入数据，使用命令`save filename`保存当前数据。
1.4 基本命令介绍常用的基本命令，如`describe`用于显示数据的基本信息、`summarize`用于计算变量的统计描述等。
第二章：数据处理与变量管理2.1 数据选择与筛选通过命令`keep`和`drop`选择和删除数据的特定变量和观察值。
2.2 数据排序与重编码使用命令`sort`对数据进行排序，使用命令`recode`对变量进行重编码。
2.3 缺失值处理介绍如何检测和处理数据中的缺失值，包括使用命令`missing`和`recode`等。
第三章：数据分析3.1 描述性统计介绍如何使用STATA计算和展示数据的描述性统计量，如均值、标准差、最大值等。
3.2 统计检验介绍如何进行常见的统计检验，如t检验、方差分析、卡方检验等。
3.3 回归分析介绍如何进行回归分析，包括一元线性回归、多元线性回归和逻辑回归等。
3.4 生存分析介绍如何进行生存分析，包括Kaplan-Meier生存曲线和Cox比例风险模型等。
第四章：图形绘制与结果解释4.1 图形绘制基础介绍如何使用STATA进行常见的数据可视化，如散点图、柱状图、折线图等。
4.2 图形选项与高级绘图介绍如何通过调整图形选项和使用高级绘图命令，进一步美化和定制图形。
Stata软件使用指南说明书
18Learning more about StataWhere to go from hereYou now know plenty enough to use Stata.There is still much,much more to learn because Stata is a rich environment for doing statistical analysis and data management.What should you do to learn more?•Get an interesting dataset and play with Stata.e the menus and dialog system to experiment with commands.Notice what commandsshow up in the Results window.You willfind that Stata’s simple and consistent commandsyntax will make the commands easy to read so that you will know what you have doneand easy to remember so that typing some commands will be faster than using menus.b.Play with graphs and the Graph Editor.•If you venture into the Command window,you willfind that many things will go faster.You will alsofind that it is possible to make mistakes where you cannot understand why Stata is balking.a.Try help commandname or Help>Stata command...and entering the command name.b.Look at the command syntax and the examples in the helpfile,and compare themwith what you pare them closely:small typographical errors make commandsimpossible for Stata to parse.•Explore Stata by selecting Help>Search....You will uncover many statistical routines that could be of great use.•Look through the Combined subject table of contents in the Stata Index.•Read and work your way through the User’s Guide.It is designed to be read from cover to cover,and it contains most of the information you need to become an expert Stata user.It is well worth reading.If you are not this ambitious and instead prefer to sample the User’s Guide and the references,there is some advice later in this chapter for you.•Browse through the reference manuals to read about statistical methods you like to use,making use of the links to jump to other topics.The reference manuals are not meant to be read from cover to cover—they are meant to be referred to as you would an encyclopedia.You canfind the datasets used in the examples in the manuals by selecting File>Example datasets...and then clicking on Stata18manual datasets.Doing so will enable you to work through the examples quickly.•Stata has much information,including answers to frequently asked questions(FAQ s),at https:///support/faqs/.•There are many useful links to Stata resources at https:///links/.Be sure to look at these materials because many outstanding resources about Stata are listed here.•Join Statalist,a forum devoted to discussion of Stata and statistics.•Read The Stata Blog:Not Elsewhere Classified at https:// to read articles written by people at Stata about all things Stata.•Visit Stata on Facebook at https:///statacorp,join Stata on Instagram at https:///statacorp,find Stata on LinkedIn at https:///company/statacorp,and follow Stata on Twitter at https:///stata to keep up with Stata.•Subscribe to the Stata Journal,which contains reviewed papers,regular columns,book reviews, and other material of interest to researchers applying statistics in a variety of disciplines.Visit https://.12[GSM]18Learning more about Stata•Many supplementary books about Stata are available.Visit the Stata Bookstore athttps:///bookstore/.•Take a Stata NetCourse R .NetCourse101is an excellent choice for learning about Stata.See https:///netcourse/for course information and schedules.•Attend a classroom or a web-based training course taught by StataCorp.Visithttps:///training/classroom-and-web/for course information and schedules.•View a webinar led by Stata developers.Visit https:///training/webinar/for the current list of topics and schedule.•Watch Stata videos at https:///user/statacorp.Suggested reading from the User’s Guide and reference manuals The User’s Guide is designed to be read from cover to cover.The reference manuals are designed as references to be sampled when necessary.Ideally,after reading this Getting Started manual,you should read the User’s Guide from cover to cover,but you probably want to become at least somewhat proficient in Stata right away.Here isa suggested reading list of sections from the User’s Guide and the reference manuals to help you onyour way to becoming a Stata expert.This list covers fundamental features and points you to some less obvious features that you might otherwise overlook.Basic elements of Stata[U]11Language syntax[U]12Data[U]13Functions and expressionsData management[U]6Managing memory[U]22Entering and importing data[D]import—Overview of importing data into Stata[D]append—Append datasets[D]merge—Merge datasets[D]compress—Compress data in memory[D]frames intro—Introduction to framesGraphics[G]Stata Graphics Reference ManualReproducible research[U]16Do-files[U]17Ado-files[U]13.5Accessing coefficients and standard errors[U]13.6Accessing results from Stata commands[U]21Creating reports[RPT]Dynamic documents intro—Introduction to dynamic documents[RPT]putdocx intro—Introduction to generating Office Open XML(.docx)files[RPT]putexcel—Export results to an Excelfile[RPT]putpdf intro—Introduction to generating PDFfiles[R]log—Echo copy of session tofile[GSM]18Learning more about Stata3Useful features that you might overlook[U]29Using the Internet to keep up to date[U]19Immediate commands[U]24Working with strings[U]25Working with dates and times[U]26Working with categorical data and factor variables[U]27Overview of Stata estimation commands[U]20Estimation and postestimation commands[R]estimates—Save and manipulate estimation resultsBasic statistics[R]anova—Analysis of variance and covariance[R]ci—Confidence intervals for means,proportions,and variances[R]correlate—Correlations of variables[D]egen—Extensions to generate[R]regress—Linear regression[R]predict—Obtain predictions,residuals,etc.,after estimation[R]regress postestimation—Postestimation tools for regress[R]test—Test linear hypotheses after estimation[R]summarize—Summary statistics[R]table intro—Introduction to tables of frequencies,summaries,and command results [R]tabulate oneway—One-way table of frequencies[R]tabulate twoway—Two-way table of frequencies[R]ttest—t tests(mean-comparison tests)Matrices[U]14Matrix expressions[U]18.5Scalars and matrices[M]Mata Reference ManualProgramming[U]16Do-files[U]17Ado-files[U]18Programming Stata[R]ml—Maximum likelihood estimation[P]Stata Programming Reference Manual[M]Mata Reference ManualSystem values[R]set—Overview of system parameters[P]creturn—Return c-class values4[GSM]18Learning more about StataInternet resourcesThe Stata website(https://)is a good place to get more information about Stata.You willfind answers to FAQ s,ways to interact with other users,official Stata updates,and other useful information.You can also join Statalist,a forum devoted to discussion of Stata and statistics.You will alsofind information on Stata NetCourses R ,which are interactive courses offered over the Internet that vary in length from a few weeks to eight weeks.Stata also offers in-person and web-based training sessions,as well as webinars on Stata features.Visit https:///learn/ for more information.At the website is the Stata Bookstore,which contains books that we feel may be of interest to Stata users.Each book has a brief description written by a member of our technical staff explaining why we think this book may be of interest.We suggest that you take a quick look at the Stata website now.You can register your copy of Stata online and request a free subscription to the Stata News.Visit https:// for information on books,manuals,and journals published by Stata Press.The datasets used in examples in the Stata manuals are available from the Stata Press website.Also visit https:// to read about the Stata Journal,a quarterly publication containing articles about statistics,data analysis,teaching methods,and effective use of Stata’s language.Visit Stata’s official blog at https:// for news and advice related to the use of Stata.The articles appearing in the blog are individually signed and are written by the same people who develop,support,and sell Stata.The Stata Blog:Not Elsewhere Classified also has links to other blogs about Stata,written by Stata users around the world.Follow Stata on Facebook at https:///statacorp,Twitter at https:///stata, Instagram at https:///statacorp,and LinkedIn athttps:///company/statacorp.You may also follow Stata on Twitter athttps:///stata fr or https:///stata es.These are good ways to stay up-to-the-minute with the latest Stata information.Watch short example videos of using Stata on YouTube at https:///user/statacorp.See[GSM]19Updating and extending Stata—Internet functionality for details on accessing official Stata updates and free additions to Stata on the Stata website.[GSM]18Learning more about Stata5 Stata,Stata Press,and Mata are registered trademarks of StataCorp LLC.Stata andStata Press are registered trademarks with the World Intellectual Property Organization®of the United Nations.Other brand and product names are registered trademarks ortrademarks of their respective companies.Copyright c 1985–2023StataCorp LLC,College Station,TX,USA.All rights reserved.。
Stata数据分析软件用户指南说明书
Jul/Aug/Sept Vol 30 No 3In the spotlight: Treatment effectsA delicate balancing actTreatment-effects modeling is a fundamental tool to obtain experimental-style causal effects from observational data. Ideally, we would conduct an experiment, but for ethical or financial reasons, an experiment sometimes is not feasible.A good example is the effect of cigarette smoking (the treatment) on the birthweight of infants (the outcome). Inan experiment, we would first obtain a representative sample of pregnant women. Then, some would be told not to smoke (the control group), while others would be forced to smoke an arbitrary number of cigarettes per day(the treatment group). Clearly, such an experiment is unethical and would not be allowed. However,we can still answer our question of interest usingStata’s suite of parametric, semiparametric,and nonparametric treatment-effectsestimators.Suppose we want to tackle thisquestion using teffects. For ourestimates to be trustworthy, we haveto guarantee that once we control forobservable characteristics, it is as if pregnant mothershad been randomly assigned to control and treatment groups.In an experiment, it is easy to inspect whether the characteristics of the treatmentand control groups are equivalent. We simply need to look at the data as observed. Forinstance, the mothers in both groups should have the same age and level of education on average, and if we plotted the density of both groups, they should look the same.However, this is not the case with observational data. Instead, we inspect whether our treatment-effects model reweights the data in such a way that the model-adjusted distribution of the mothers’ characteristics is equivalent across groups.The balancing act in actionWe model the birthweight (bweight) as a function of the number of prenatal visits (nprenatal), whether the mother is married (mmarried), and whether this baby is her first pregnancy (fbaby). The treatment, smoking during pregnancy (mbsmoke), is modeled as a function of the same variables and with regard to whether the mother consumed alcohol during her pregnancy. We type. webuse cattaneo2, clear(Excerpt from Cattaneo (2010) Journal of Econometrics 155: 138-154). teffects ipwra (bweight nprenatal i.mmarried i.fbaby)(mbsmoke i.mmarried i.alcohol i.fbaby nprenatal)We do not show the output, but suffice it to say that the effect of smoking is large and decidedly significant.Continued on p. 22The values in the Raw columns show that without controlling for covariates, the groups are very different. The values in the Weighted columns show the differences in means and the ratio of the variances of the control and treatment groups after reweighting for the covariates. The mean differences are all near zero, and the variance ratios are all close to one. These diagnostics suggest that after we control for the covariates, it is as if we had randomly assigned the mothers to either the control group or the treatment group.W e can also inspect this graphically by plotting the distribution before fitting our model and the distribution after weighting. W e do this for the number of prenatal visits. .tebalance density nprenatalThe density graphs confirm what we observe from our diagnostics.Can we do a test?What we have described so far is qualitative: we have diagnostics but not a formal test. We can, however, do a test. Intuitively, the score equations for the treatment and control groups should be the same. We can test whether this is the case by using the score equations as moments in an overidentification test. The null hypothesis is that our covariates are balanced. We type. tebalance overidOveridentification test for covariate balanceH0: Covariates are balanced:chi2(5) = 4.0425Prob > chi2 = 0.5433We cannot reject the null hypothesis. This impliesthat there is no evidence that our covariates remain imbalanced after reweighting.Parting wordsSometimes, we cannot conduct experiments, butwe can obtain experimental-style causal effects from observational data. For this to happen, we need to be able to say that our treatment-effects model reweights the data in such a way that the model-adjusted distribution ofthe covariates is equivalent across treatment groups. We can verify this with the postestimation diagnostic tests provided in teffects.—Enrique Pinzon Senior Econometrician, StataCorpT o obtain balancing diagnostics of the averages and variances of the mothers’ characteristics across groups, we type3 In the spotlight: irtNew to Stata 14 is a suite of commands to fit item response theory (IRT) models. IRT models are used to analyze the relationship between the latent trait of interest and the items intended to measure the trait. Stata’s irt commands provide easy access to some of the commonly used IRT models for binary and polytomous responses, and irtgraph commands can be used to plot characteristic functions and information functions.T o learn more about Stata’s IRT features, refer to the [IRT] Item Response Theory Reference Manual; here I want to go beyond the manual and show you some examples of what you can do with a little bit of Stata code.The dataset used in the examples contains answers to nine binary items, q1–q9. I do not show much Stata code here; see the accompanying blog entry at for details, including replication code.Example 1T o get started, I want to show you how simple IRT analysis is in Stata.When I use the nine binary items q1–q9, all I need to type to fit a 1PL model is. irt 1pl q*Equivalently, I can use a dash notation or explicitly spell out the variable names:. irt 1pl q1-q9. irt 1pl q1 q2 q3 q4 q5 q6 q7 q8 q9I can also use parenthetical notation:. irt (1pl q1-q9)Parenthetical notation is not very useful for a simple IRT model, but it comes in handy when you want to fit a single IRT model to combinations of binary, ordinal, and nominal items:. irt (1pl q1-q5) (1pl q6-q9) (pcm x1-x10) ...IRT graphs are equally simple to create in Stata. For example, to plot item characteristic curves (ICCs) for all the items in a model, I type. irtgraph iccY es, that’s it!Example 2Sometimes, I want to fit the same IRT model on two different groups and see how the estimated parameters differ between the groups. This exercise can be part of investigating differential item functioning (DIF) or parameter invariance.I split the data into two groups, fit two separate 2PL models, and create two scatterplots to see how close the parameter estimates for discrimination and difficulty are for the two groups. For simplicity, my group variable is 1 for odd-numbered observations and 0 for even-numbered observations.We see that the estimated parametersfor item q8 appear to differ betweenthe two groups.Example 3Continuing with the example above,I want to show you how to use alikelihood-ratio test to test for item-parameter differences between groups.4Using item q8 as an example, I want to fit one model that constrains item q8 parameters to be the same between the two groups and fit another model that allows these parameters to vary.The first model is easy. I can fit a 2PL model for the entire dataset, which implicitly constrains the parameters to be equal for both groups. I store the estimates under the name equal.. quietly irt 2pl q*. estimates store equalT o estimate the second model, I need the following:. irt (2pl q1-q7 q9) (2pl q8 if odd) (2pl q8 if !odd)Unfortunately, this is illegal syntax. I can, however, split the item into two new variables where each variable is restricted to the required subsample:. generate q8_1 = q8 if odd. generate q8_2 = q8 if !oddI estimate the second IRT model, this time with items q8_1 and q8_2 taking the place of the original q8:. quietly irt 2pl q1-q7 q8_1 q8_2 q9. estat report q8_1 q8_2Two-parameter logistic model Number of obs = 800Log likelihood = -4116.2064Coef. Std. Err. z P>|z| [95% Conf. Interval]q8_1Discrim | 1.095867 .2647727 4.14 0.000 .5769218 1.614812Diff -1.886126 .3491548 -5.40 0.000 -2.570457 -1.201795q8_2Discrim 1.93005 .4731355 4.08 0.000 1.002721 2.857378Diff -1.544908 .2011934 -7.68 0.000 -1.93924 -1.150577Now, I can perform the likelihood-ratio test:. lrtest equal ., forceLikelihood-ratio test LR chi2(2) = 4.53(Assumption: equal nested in .) Prob > chi2 = 0.1040The test suggests the first model is preferable even though the two ICCs clearly differ:SummaryIRT models are used to analyze the relationship betweenthe latent trait of interest and the items intended tomeasure the trait. Stata’s irt commands provide easyaccess to some of the commonly used IRT models, andirtgraph commands implement the most commonlyused IRT plots. With just a few extra steps, you can easilycreate customized graphs, such as the ones demonstratedabove, which incorporate information from separate IRTmodels. Don’t forget to see the accompanying blog entryat that shows the Stata code used in thisarticle.—Rafal RaciborskiSenior Statistical Developer, StataCorp5 NetCourses®New: Introduction toStatistical Graphics Using StataLearn how to communicate your data with Stata’spowerful graphics features. This course will introducedifferent kinds of graphs and demonstrate how to usethem for exploratory data analysis. T opics include howto use graphs to check model assumptions, how toformat, save, and export your graphs for publicationusing the Graph Editor, how to create custom graphschemes, how to create complex graphs by layering andcombining multiple graphs, how to use margins andmarginsplot, and more. The course also contains 94videos with detailed, step-by-step explanations of thedifferent graphs discussed in the course. Bonus materialincludes information on user-written graph commandsand useful data management tools.September 11–October 23, 2015 ......................$150.00Don’t forget our other NetCourses!Introduction to StataLearn how to use all of Stata’s tools and become a sophisticated Stata user. Y ou will understand the Stata environment, how to import and export data from different formats, how Stata’s intuitive syntax works, data management in Stata, and more.September 11–October 23, 2015 ........................$95.00 Introduction to Stata ProgrammingBecome an expert in organizing your work in Stata. Make the most of Stata’s scripting language to improve your workflow and create concretely reproducible analyses. Learn how to speed up your work and do more complete analyses.September 11–October 23, 2015 ......................$150.00 Advanced Stata ProgrammingLearn how to create and debug your own commands that are indistinguishable from the commands that ship with Stata. September 18–November 6, 2015 .....................$175.00Introduction to Univariate Time Series with StataLearn univariate time-series analysis with an emphasis on the practical aspects most needed by practitioners and applied researchers.September 18–November 6, 2015 .....................$295.00Introduction to Panel Data Using Stata Become an expert in the analysis and implementation oflinear, nonlinear, and dynamic panel-data estimators using Stata. Geared for researchers and practitioners in all fields, this course focuses on the interpretation of panel-data estimates and the assumptions underlying the models that give rise to them.September 25–November 6, 2015 .....................$295.00Introduction to Survival AnalysisUsing StataLearn how to effectively analyze survival data using Stata. We cover censoring, truncation, hazard rates, and survival functions. Discover how to set the survival-time characteristics of your dataset just once and apply any of Stata’s many estimators and statistics to those data. September 18–November 6, 2015 .....................$295.00Learn more and enroll:/netcourseThe dates above don’t work for you? No problem! NetCourseNow allows you to set the time and workat your own pace as well. It also gives you a personal NetCourse instructor to guide you through the course./netcourse/ncnow7CONFERENCE/chicago16#stata2016Keep up with future Stata Users Group meetings. We post our schedule at /meeting . Want to be notified when new meeting information is posted? Go to /alerts and sign up for an email alert today.2015 International Stata Users Group meetingsStockholm, Sweden September 4, /meeting/nordic-and-baltic15Canberra, Australia September 24–25, /meeting/australia15London, UKSeptember 10–11, 2015/meeting/uk15Madrid, SpainOctober 22, 2015/meeting/spain15Florence, Italy November 12–13, /meeting/italy15Lisbon, Portugal September 18, /meeting/portugal15Reaching new heightsJuly 28–29, 2016, at the Gleacher CenterCHICAGO20168Contact us979-696-4600 979-696-4601 (fax)***************** Please include your Stata serial number with all correspondence.Find a Stata distributor near you:/worldwideCopyright 2015 by StataCorp LP . Stata is a registered trademark of StataCorp LP .Public training scheduleUsing Stata Effectively: DataManagement, Analysis, and Graphics FundamentalsSeptember 22–23, 2015, Washington, DC October 13–14, 2015, Washington, DCAimed at both new Stata users and those who wish to learn techniques for efficient day-to-day use of Stata, this course enables you to use Stata in a reproducible manner, making collaborative changes and follow-up analyses much simpler. Exercises and Stata examples supplement the lessons.Survey Data Analysis Using StataOctober 15–16, 2015, Washington, DCSet up and analyze data from complex survey designs. The course covers the sampling methods used to collect survey data and how they affect the estimation of totals, ratios, and regression coefficients. The course also covers Stata’s support for many survey variance estimators,including linearization, balanced and repeated replications (BRR), and jackknife.Multilevel/Mixed Models Using Stata• Bayesian analysis• IRT (item response theory)• Unicode• Integration with Excel • More in treatment effects • Multilevel survival models • More in multilevel models• Denominator degrees of freedom • More in SEM• More in power and sample size • Markov-switching models • Panel-data survival models • Fractional outcome regression• Marginal means and marginal effects • Hurdle models。
stata操作指南
stata操作指南计量经济学stata操作（实验课）第一章stata基本知识1、stata窗口介绍2、基本操作（1）窗口锁定：Edit-preferences-general preferences-windowing-lock splitter （2）数据导入（3）打开文件：use E:\example.dta,clear（4）日期数据导入：gen newvar=date(varname, “ymd”)format newvar %td 年度数据gen newvar=monthly(varname, “ym”)format newvar %tm 月度数据gen newvar=quarterly(varname, “yq”)format newvar %tq 季度数据（5）变量标签Label variable tc ` “total output” ’（6）审视数据describelist x1 x2list x1 x2 in 1/5list x1 x2 if q>=1000drop if q>=1000keep if q>=1000（6）考察变量的统计特征summarize x1su x1 if q>=10000su q,detailsutabulate x1correlate x1 x2 x3 x4 x5 x6（7）画图histogram x1, width(1000) frequency kdensity x1scatter x1 x2twoway (scatter x1 x2) (lfit x1 x2) twoway (scatter x1 x2) (qfit x1 x2) （8）生成新变量gen lnx1=log(x1)gen q2=q^2gen lnx1lnx2=lnx1*lnx2gen larg=(x1>=10000)rename larg largeg large=(q>=6000)replace large=(q>=6000)drop ln*（8）计算功能display log(2)（9）线性回归分析regress y1 x1 x2 x3 x4vce #显示估计系数的协方差矩阵reg y1 x1 x2 x3 x4,noc #不要常数项reg y1 x1 x2 x3 x4 if q>=6000reg y1 x1 x2 x3 x4 if largereg y1 x1 x2 x3 x4 if large==0reg y1 x1 x2 x3 x4 if ～large predict yhatpredict e1,residualdisplay 1/_b[x1]test x1=1 # F检验，变量x1的系数等于1test (x1=1) (x2+x3+x4=1) # F联合假设检验test x1 x2 #系数显著性的联合检验testnl _b[x1]= _b[x2]^2（10）约束回归constraint def 1 x1+x2+x3=1cnsreg y1 x1 x2 x3 x4,c(1)cons def 2 x4=1cnsreg y1 x1 x2 x3 x4,c(1-2)（11）stata的日志File-log-begin-输入文件名log off 暂时关闭log on 恢复使用log close 彻底退出（12）stata命令库更新Update allhelp command第二章有关大样本ols的stata命令及实例（1）ols估计的稳健标准差reg y x1 x2 x3,robust（2）实例use example.dta,clearreg y1 x1 x2 x3 x4test x1=1reg y1 x1 x2 x3 x4,rtestnl _b[x1]=_b[x2]^2第三章最大似然估计法的stata命令及实例（1）最大似然估计help ml（2）LR检验lrtest #对面板数据中的异方差进行检验（3）正态分布检验sysuse auto #调用系统数据集auto.dtahist mpg,normalkdensity mpg,normalqnorm mpg*手工计算JB统计量sum mpg,detaildi (r(N)/6)*((r(skewness)^2)+[(1/4)*(r(kurtosis)-3)^2]) di chi2tail(自由度，上一步计算值)*下载非官方程序ssc install jb6jb6 mpg*正态分布的三个检验sktest mpgswilk mpgsfrancia mpg*取对数后再检验gen lnmpg=log(mpg)kdensity lnmpg, normaljb6 lnmpgsktest lnmpg第四章处理异方差的stata命令及实例（1）画残差图rvfplotrvfplot varname*例题use example.dta,clearreg y x1 x2 x3 x4rvfplot # 与拟合值的散点图rvfplot x1 # 画残差与解释变量的散点图（2）怀特检验estat imtest,white*下载非官方软件ssc install whitetst（3）BP检验estat hettest #默认设置为使用拟合值estat hettest,rhs #使用方程右边的解释变量estat hettest [varlist] #指定使用某些解释变量estat hettest,iidestat hettest,rhs iidestat hettest [varlist],iid（4）WLSreg y x1 x2 x3 x4 [aw=1/var]*例题quietly reg y x1 x2 x3 x4predict e1,resgen e2=e1^2gen lne2=log(e2)reg lne2 x2,nocpredict lne2fgen e2f=exp(lne2f)reg y x1 x2 x3 x4 [aw=1/e2f]（5）stata命令的批处理（写程序）Window-do-file editor-new do-file#WLS for examplelog using E:\wls_example.smcl,replaceset more offuse E:\example.dta,clearreg y x1 x2 x3 x4predict e1,resgen e2=e1^2g lne2=log(e2)reg lne2 x2,nocpredict lne2fg e2f=exp(lne2f)*wls regressionreg y x1 x2 x3 x4 [aw=1/e2f]log closeexit第五章处理自相关的stata命令及实例（1）滞后算子/差分算子tsset yearl.l2.D.D2.LD.（2）画残差图scatter e1 l.e1ac e1pac e1（3）BG检验estat bgodfrey（默认p=1）estat bgodfrey,lags(p)estat bgodfrey,nomiss0（使用不添加0的BG检验）（4）Ljung-Box Q检验reg y x1 x2 x3 x4predict e1,residwntestq e1wntestq e1,lags(p)* wntestq指的是“white noise test Q”，因为白噪声没有自相关（5）DW检验做完OLS回归后，使用estat dwatson（6）HAC稳健标准差newey y x1 x2 x3 x4,lag(p)reg y x1 x2 x3 x4,cluster(varname)（7）处理一阶自相关的FGLSprais y x1 x2 x3 x4 （使用默认的PW估计方法）prais y x1 x2 x3 x4,corc （使用CO估计法）（8）实例use icecream.dta, cleartsset timegraph twoway connect consumption temp100 time, msymbol(circle) msymbol(triangle) reg consumption temp price incomepredict e1, resg e2=l.e1twoway (scatter e1 e2) (lfit e1 e2)ac e1pac e1estat bgodfreywntestq e1estat dwatsonnewey consumption temp price income, lag (3)prais consumption temp price income, corcprais consumption temp price income, nologreg consumption temp l.temp price incomeestat bgodfreyestat dwatson第六章模型设定与数据问题（1）解释变量的选择reg y x1 x2 x3estat ic*例题use icecream.dta, clearreg consumption temp price incomeestat icreg consumption temp l.temp price incomeestat ic（2）对函数形式的检验（reset检验）reg y x1 x2 x3estat ovtest （使用被解释变量的2、3、4次方作为非线性项）estat ovtest, rhs （使用解释变量的幂作为非线性项，ovtest-omitted variable test）*例题use nerlove.dta, clearreg lntc lnq lnpl lnpk lnpfestat ovtestg lnq2=lnq^2reg lntc lnq lnq2 lnpl lnpk lnpfestat ovtest（3）多重共线性estat vif*例题use nerlove.dta, clearreg lntc lnq lnpl lnpk lnpfestat vif（4）极端数据reg y x1 x2 x3predict lev, leverage （列出所有解释变量的lev值）gsort –levsum levlist lev in 1/3*例题use nerlove.dta, clearquietly reg lntc lnq lnpl lnpk lnpfpredict lev, leveragesum levgsort –levlist lev in 1/3（5）虚拟变量gen d=(year>=1978)tabulate province, generate (pr)reg y x1 x2 x3 pr2-pr30（6）经济结构变动的检验方法1：use consumption_china.dta, cleargraph twoway connect c y year, msymbol(circle) msymbol(triangle)reg c yreg c y if year<1992reg c y if year>=1992计算F统计量方法2：gen d=(year>1991)gen yd=y*dreg c y d ydtest d yd第七章工具变量法的stata命令及实例（1）2SLS的stata命令ivregress 2sls depvar [varlist1] (varlist2=instlist)如：ivregress 2sls y x1 (x2=z1 z2)ivregress 2sls y x1 (x2 x3=z1 z2 z3 z4) ,r firstestat firststage,all forcenonrobust （检验弱工具变量的命令）ivregress liml depvar [varlist 1] (varlist2=instlist)estat overid （过度识别检验的命令）*对解释变量内生性的检验（hausman test），缺点：不适合于异方差的情形reg y x1 x2estimates store olsivregress 2sls y x1 (x2=z1 z2)estimates store ivhausman iv ols, constant sigmamore*DWH检验estat endogenous*GMM的过度识别检验ivregress gmm y x1 (x2=z1 z2) （两步GMM）ivregress gmm y x1 (x2=z1 z2),igmm （迭代GMM）estat overid*使用异方差自相关稳健的标准差GMM命令ivregress gmm y x1 (x2=z1 z2), vce (hac nwest[#])（2）实例use grilic.dta,clearsumcorr iq sreg lw s expr tenure rns smsa,rreg lw s iq expr tenure rns smsa,rivregress 2sls lw s expr tenure rns smsa (iq=med kww mrt age),restat overidivregress 2sls lw s expr tenure rns smsa (iq=med kww),r first estat overidestat firststage, all forcenonrobust （检验工具变量与内生变量的相关性）ivregress liml lw s expr tenure rns smsa (iq=med kww),r *内生解释变量检验quietly reg lw s iq expr tenure rns smsaestimates store olsquietly ivregress 2sls lw s expr tenure rns smsa (iq=med kww) estimates store ivhausman iv ols, constant sigmamoreestat endogenous （存在异方差的情形）*存在异方差情形下，GMM比2sls更有效率ivregress gmm lw s expr tenure rns smsa (iq=med kww)estat overidivregress gmm lw s expr tenure rns smsa (iq=med kww),igmm*将各种估计方法的结果存储在一张表中quietly ivregress gmm lw s expr tenure rns smsa (iq=med kww)estimates store gmmquietly ivregress gmm lw s expr tenure rns smsa (iq=med kww),igmmestimates store igmmestimates table gmm igmm第八章短面板的stata命令及实例（1）面板数据的设定xtset panelvar timevarencode country,gen(cntry) （将字符型变量转化为数字型变量）xtdesxtsumxttab varnamextline varname,overlay*实例use traffic.dta,clearxtset state yearxtdesxtsum fatal beertax unrate state yearxtline fatal（2）混合回归reg y x1 x2 x3,vce(cluster id)如：reg fatal beertax unrate perinck,vce(cluster state)estimates store ols对比：reg fatal beertax unrate perinck（3）固定效应xtreg y x1 x2 x3,fe vce(cluster id)xi:reg y x1 x2 x3 i.id,vce(cluster id) （LSDV法）xtserial y x1 x2 x3,output （一阶差分法，同时报告面板一阶自相关）estimates store FD*双向固定效应模型tab year, gen (year)xtreg fatal beertax unrate perinck year2-year7, fe vce (cluster state)estimates store FE_TWtest year2 year3 year4 year5 year6 year7（4）随机效应xtreg y x1 x2 x3,re vce(cluster id) （随机效应FGLS）xtreg y x1 x2 x3,mle （随机效应MLE）xttest0 （在执行命令xtreg, re 后执行，进行LM检验）（5）组间估计量xtreg y x1 x2 x3,be（6）固定效应还是随机效应：hausman testxtreg y x1 x2 x3,feestimates store fextreg y x1 x2 x3,reestimates store rehausman fe re,constant sigmamore （若使用了vce(cluster id)，则无法直接使用该命令，解决办法详见P163）estimates table ols fe_robust fe_tw re be, b se （将主要回归结果列表比较）第九章长面板与动态面板（1）仅解决组内自相关的FGLSxtpcse y x1 x2 x3 ,corr(ar1) （具有共同的自相关系数）xtpcse y x1 x2 x3 ,corr(psar1) （允许每个面板个体有自身的相关系数）例题：use mus08cigar.dta,cleartab state,gen(state)gen t=year-62reg lnc lnp lnpmin lny state2-state10 t,vce(cluster state)estimates store OLSxtpcse lnc lnp lnpmin lny state2-state10 t,corr(ar1) (考虑存在组内自相关，且各组回归系数相同)estimates store AR1xtpcse lnc lnp lnpmin lny state2-state10 t,corr(psar1) (考虑存在组内自相关，且各组回归系数不相同)estimates store PSAR1xtpcse lnc lnp lnpmin lny state2-state10 t, hetonly (仅考虑不同个体扰动性存在异方差，忽略自相关)estimates store HETONL Yestimates table OLS AR1 PSAR1 HETONL Y, b se（2）同时处理组内自相关与组间同期相关的FGLSxtgls y x1 x2 x3,panels (option/iid/het/cor) corr(option/ar1/psar1) igls注：执行上述xtpcse、xtgls命令时，如果没有个体虚拟变量，则为随机效应模型；如果加上个体虚拟变量，则为固定效应模型。
Stata 基础参考手册说明书
Contentsintro............................................Introduction to base reference manual about............................................Display information about your Stata adoupdate................................................Update user-written ado-files ameans.....................................Arithmetic,geometric,and harmonic means anova.............................................Analysis of variance and covariance anova postestimation......................................Postestimation tools for anova areg..................................Linear regression with a large dummy-variable set areg postestimation........................................Postestimation tools for areg asclogit....................Alternative-specific conditional logit(McFadden’s choice)model asclogit postestimation..................................Postestimation tools for asclogit asmprobit...............................Alternative-specific multinomial probit regression asmprobit postestimation..............................Postestimation tools for asmprobit asroprobit..............................Alternative-specific rank-ordered probit regression asroprobit postestimation..............................Postestimation tools for asroprobit BIC note.............................................Calculating and interpreting BIC binreg........................Generalized linear models:Extensions to the binomial family binreg postestimation....................................Postestimation tools for binreg biprobit....................................................Bivariate probit regression biprobit postestimation..................................Postestimation tools for biprobit bitest.......................................................Binomial probability test bootstrap...........................................Bootstrap sampling and estimation bootstrap postestimation................................Postestimation tools for bootstrap boxcox..................................................Box–Cox regression models boxcox postestimation....................................Postestimation tools for boxcox brier......................................................Brier score decomposition bsample..................................................Sampling with replacement bstat........................................................Report bootstrap results centile...........................................Report centile and confidence interval ci...............................Confidence intervals for means,proportions,and counts clogit......................................Conditional(fixed-effects)logistic regression clogit postestimation......................................Postestimation tools for clogit plementary log-log regression cloglog postestimation..................................Postestimation tools for cloglog cls............................................................Clear Results window cnsreg...................................................Constrained linear regression cnsreg postestimation....................................Postestimation tools for cnsreg constraint..................................................Define and list constraints contrast..............................Contrasts and linear hypothesis tests after estimation contrast postestimation..................................Postestimation tools for contrast copyright...............................................Display copyright information copyright apache.........................................Apache copyright notification copyright boost............................................Boost copyright notification copyright freetype.......................................FreeType copyright notification copyright icu...............................................ICU copyright notification copyright jagpdf.........................................JagPDF copyright notificationiii Contentscopyright PACK copyright notification copyright libpng..........................................libpng copyright notification copyright miglayout..................................MiG Layout copyright notification copyright scintilla........................................Scintilla copyright notification copyright ttf2pt1..........................................ttf2pt1copyright notification copyright zlib..............................................zlib copyright notification correlate.............................Correlations(covariances)of variables or coefficients cumul.......................................................Cumulative distribution cusum........................................Cusum plots and tests for binary variables unch dialog diagnostic plots..........................................Distributional diagnostic plots display................................................Substitute for a hand calculator do....................................................Execute commands from afile doedit................................................Edit do-files and other textfiles parative scatterplots dstdize.............................................Direct and indirect standardization dydx........................................Calculate numeric derivatives and integrals eform option......................................Displaying exponentiated coefficients eivreg...................................................Errors-in-variables regression eivreg postestimation.....................................Postestimation tools for eivreg error messages.........................................Error messages and return codes esize............................................Effect size based on mean comparison estat........................................................Postestimation statistics estat classification......................................Classification statistics and table estat gof...............................Pearson or Hosmer–Lemeshow goodness-of-fit test estat ic...................................................Display information criteria estat summarize..........................................Summarize estimation sample estat vce...........................................Display covariance matrix estimates estimates........................................Save and manipulate estimation results estimates describe..........................................Describe estimation results estimates for..............................Repeat postestimation command across models estimates notes.........................................Add notes to estimation results estimates replay............................................Redisplay estimation results estimates save..........................................Save and use estimation results estimates stats...............................................Model-selection statistics estimates store.......................................Store and restore estimation results estimates pare estimation results estimates title............................................Set title for estimation results estimation options.................................................Estimation options exit.....................................................................Exit Stata exlogistic...................................................Exact logistic regression exlogistic postestimation................................Postestimation tools for exlogistic expoisson...................................................Exact Poisson regression expoisson postestimation...............................Postestimation tools for expoisson fp...................................................Fractional polynomial regression fp postestimation............................................Postestimation tools for fp frontier....................................................Stochastic frontier models frontier postestimation..................................Postestimation tools for frontier fvrevar.................................Factor-variables operator programming commandContents iiifvset..................................................Declare factor-variable settings gllamm......................................Generalized linear and latent mixed models glm.......................................................Generalized linear models glm postestimation........................................Postestimation tools for glm glogit......................................Logit and probit regression for grouped data glogit postestimation..............Postestimation tools for glogit,gprobit,blogit,and bprobit gmm........................................Generalized method of moments estimation gmm postestimation......................................Postestimation tools for gmm grmeanby..............................Graph means and medians by categorical variables hausman...................................................Hausman specification test heckman...................................................Heckman selection model heckman postestimation................................Postestimation tools for heckman heckoprobit.................................Ordered probit model with sample selection heckoprobit postestimation............................Postestimation tools for heckoprobit heckprobit.........................................Probit model with sample selection heckprobit postestimation..............................Postestimation tools for heckprobit help..........................................................Display help in Stata hetprobit.................................................Heteroskedastic probit model hetprobit postestimation................................Postestimation tools for hetprobit histogram.............................Histograms for continuous and categorical variables icc...................................................Intraclass correlation coefficients inequality.......................................................Inequality measures intreg...........................................................Interval regression intreg postestimation......................................Postestimation tools for intreg ivpoisson.................................Poisson regression with endogenous regressors ivpoisson postestimation................................Postestimation tools for ivpoisson ivprobit..............................Probit model with continuous endogenous regressors ivprobit postestimation..................................Postestimation tools for ivprobit ivregress................................Single-equation instrumental-variables regression ivregress postestimation................................Postestimation tools for ivregress ivtobit................................Tobit model with continuous endogenous regressors ivtobit postestimation....................................Postestimation tools for ivtobit jackknife.......................................................Jackknife estimation jackknife postestimation................................Postestimation tools for jackknife kappa..........................................................Interrater agreement kdensity...........................................Univariate kernel density estimation ksmirnov..............................Kolmogorov–Smirnov equality-of-distributions test kwallis.................................Kruskal–Wallis equality-of-populations rank test dder of powers level.....................................................Set default confidence level limits.....................................................Quick reference for limits lincom..............................................Linear combinations of estimators linktest.................................Specification link test for single-equation models lnskew0..................................Find zero-skewness log or Box–Cox transform log.....................................................Echo copy of session tofile logistic........................................Logistic regression,reporting odds ratios logistic postestimation....................................Postestimation tools for logistic logit..........................................Logistic regression,reporting coefficientsiv Contentslogit postestimation........................................Postestimation tools for logit rge one-way ANOV A,random effects,and reliability lowess..........................................................Lowess smoothing lpoly.......................................Kernel-weighted local polynomial smoothing pute area under ROC curve and graph the curve lrtest..............................................Likelihood-ratio test after estimation lsens............................Graph sensitivity and specificity versus probability cutoff lv.............................................................Letter-value displays margins.........................Marginal means,predictive margins,and marginal effects margins postestimation..................................Postestimation tools for margins margins,contrast................................................Contrasts of margins margins,pwcompare...................................Pairwise comparisons of margins marginsplot...............................Graph results from margins(profile plots,etc.) matsize................................Set the maximum number of variables in a model maximize.............................................Details of iterative maximization mean...............................................................Estimate means mean postestimation......................................Postestimation tools for mean meta................................................................Meta-analysis mfp.........................................Multivariable fractional polynomial models mfp postestimation........................................Postestimation tools for mfp misstable....................................................Tabulate missing values mkspline..................................Linear and restricted cubic spline construction ml...................................................Maximum likelihood estimation mlexp........................Maximum likelihood estimation of user-specified expressions mlexp postestimation.....................................Postestimation tools for mlexp mlogit......................................Multinomial(polytomous)logistic regression mlogit postestimation....................................Postestimation tools for mlogit more........................................................The—more—message mprobit.................................................Multinomial probit regression mprobit postestimation..................................Postestimation tools for mprobit nbreg...................................................Negative binomial regression nbreg postestimation............................Postestimation tools for nbreg and gnbreg nestreg.......................................................Nested model statistics net............................Install and manage user-written additions from the Internet net search....................................Search the Internet for installable packages netio....................................................Control Internet connections news.............................................................Report Stata news nl.................................................Nonlinear least-squares estimation nl postestimation............................................Postestimation tools for nl nlcom............................................Nonlinear combinations of estimators nlogit........................................................Nested logit regression nlogit postestimation......................................Postestimation tools for nlogit nlsur......................................Estimation of nonlinear systems of equations nlsur postestimation......................................Postestimation tools for nlsur nptrend............................................Test for trend across ordered groups ologit....................................................Ordered logistic regression ologit postestimation......................................Postestimation tools for ologit oneway.................................................One-way analysis of variance oprobit.....................................................Ordered probit regressionContents v oprobit postestimation....................................Postestimation tools for oprobit orthog........................Orthogonalize variables and compute orthogonal polynomials pcorr......................................Partial and semipartial correlation coefficients permute................................................Monte Carlo permutation tests pk...........................................Pharmacokinetic(biopharmaceutical)data pkcollapse................................Generate pharmacokinetic measurement dataset pkcross................................................Analyze crossover experiments pkequiv.................................................Perform bioequivalence tests pkexamine.........................................Calculate pharmacokinetic measures pkshape....................................Reshape(pharmacokinetic)Latin-square data pksumm.............................................Summarize pharmacokinetic data poisson..........................................................Poisson regression poisson postestimation..................................Postestimation tools for poisson predict................................Obtain predictions,residuals,etc.,after estimation predictnl..................Obtain nonlinear predictions,standard errors,etc.,after estimation probit.............................................................Probit regression probit postestimation......................................Postestimation tools for probit proportion......................................................Estimate proportions proportion postestimation..............................Postestimation tools for proportion prtest..........................................................Tests of proportions pwcompare.....................................................Pairwise comparisons pwcompare postestimation............................Postestimation tools for pwcompare pwmean...............................................Pairwise comparisons of means pwmean postestimation..................................Postestimation tools for pwmean qc............................................................Quality control charts qreg............................................................Quantile regression qreg postestimation...................Postestimation tools for qreg,iqreg,sqreg,and bsqreg query.....................................................Display system parameters ranksum.............................................Equality tests on unmatched data ratio...............................................................Estimate ratios ratio postestimation........................................Postestimation tools for ratio reg3.........................Three-stage estimation for systems of simultaneous equations reg3postestimation........................................Postestimation tools for reg3 regress...........................................................Linear regression regress postestimation....................................Postestimation tools for regress regress postestimation diagnostic plots......................Postestimation plots for regress regress postestimation time series............Postestimation tools for regress with time series #review...................................................Review previous commands roc.....................................Receiver operating characteristic(ROC)analysis roccomp...............................................Tests of equality of ROC areas rocfit........................................................Parametric ROC models rocfit postestimation......................................Postestimation tools for rocfit rocreg.................................Receiver operating characteristic(ROC)regression rocreg postestimation....................................Postestimation tools for rocreg rocregplot.....................Plot marginal and covariate-specific ROC curves after rocreg roctab...................................................Nonparametric ROC analysis rologit................................................Rank-ordered logistic regression rologit postestimation....................................Postestimation tools for rologit rreg..............................................................Robust regressionvi Contentsrreg postestimation........................................Postestimation tools for rreg runtest.......................................................Test for random order scobit.....................................................Skewed logistic regression scobit postestimation......................................Postestimation tools for scobit sdtest.....................................................Variance-comparison tests search...................................Search Stata documentation and other resources serrbar................................................Graph standard error bar chart set....................................................Overview of system parameters set cformat........................................Format settings for coefficient tables set defaults............................Reset system parameters to original Stata defaults set emptycells.............................Set what to do with empty cells in interactions set seed....................................Specify initial value of random-number seed set showbaselevels..................................Display settings for coefficient tables signrank...............................................Equality tests on matched data simulate....................................................Monte Carlo simulations sj........................................Stata Journal and STB installation instructions sktest.........................................Skewness and kurtosis test for normality slogit..................................................Stereotype logistic regression slogit postestimation......................................Postestimation tools for slogit smooth...................................................Robust nonlinear smoother spearman........................................Spearman’s and Kendall’s correlations spikeplot..................................................Spike plots and rootograms ssc...........................................Install and uninstall packages from SSC stem.........................................................Stem-and-leaf displays stepwise........................................................Stepwise estimation stored results.........................................................Stored results suest..................................................Seemingly unrelated estimation summarize.......................................................Summary statistics sunflower..........................................Density-distribution sunflower plots sureg..........................................Zellner’s seemingly unrelated regression sureg postestimation......................................Postestimation tools for sureg swilk..............................Shapiro–Wilk and Shapiro–Francia tests for normality symmetry....................................Symmetry and marginal homogeneity tests table..............................................Flexible table of summary statistics pact table of summary statistics tabulate oneway..........................................One-way table of frequencies tabulate twoway..........................................Two-way table of frequencies tabulate,summarize().......................One-and two-way tables of summary statistics test.............................................Test linear hypotheses after estimation testnl.........................................Test nonlinear hypotheses after estimation tetrachoric...................................Tetrachoric correlations for binary variables tnbreg..........................................Truncated negative binomial regression tnbreg postestimation....................................Postestimation tools for tnbreg tobit..............................................................Tobit regression tobit postestimation........................................Postestimation tools for tobit total................................................................Estimate totals total postestimation........................................Postestimation tools for total tpoisson.................................................Truncated Poisson regression tpoisson postestimation..................................Postestimation tools for tpoisson translate......................................................Print and translate logsContents vii truncreg........................................................Truncated regression truncreg postestimation..................................Postestimation tools for truncreg ttest...................................................t tests(mean-comparison tests) update.....................................................Check for official updates vce option.......................................................Variance estimators view............................................................Viewfiles and logs vwls.................................................Variance-weighted least squares vwls postestimation........................................Postestimation tools for vwls which......................................Display location and version for an ado-file xi............................................................Interaction expansion zinb.........................................Zero-inflated negative binomial regression zinb postestimation........................................Postestimation tools for zinb zip..................................................Zero-inflated Poisson regression zip postestimation..........................................Postestimation tools for zip Author index..................................................................... Subject index.....................................................................。
Stata数据分析软件用户手册说明书
Title Stata—Stata interface functionsContents Description Remarks and examples Reference Also seeContents[M-5]Manual entry Function PurposeAccess to datast nvar()st nvar()number of variablesst nobs()number of observationsst data()st data()load numeric data from Stata into matrixst sdata()load string data from Stata into matrix st store()st store()store numeric data in Stata datasetst sstore()store string data in Stata dataset st view()st view()make view onto Stata datasetst sview()same;string variablesst subview()st subview()make view from viewst viewvars()st viewvars()identify variables and observationsst viewobs()corresponding to viewVariable names&indicesst varindex()st varindex()variable indices from variable namesst varname()st varname()variable names from variable indices12Stata—Stata interface functionsVariable characteristicsst varrename()st varrename()rename Stata variablest vartype()st vartype()storage type of Stata variablest isnumvar()whether variable is numericst isstrvar()whether variable is stringst isalias()st isalias()whether variable is aliasst aliasframe()linked frame for aliasst aliaslinkname()link variable for aliasst aliasvarname()target variable for aliasst varformat()st varformat()obtain/set format of Stata variablest varlabel()obtain/set variable labelst varvaluelabel()obtain/set value labelst vlexists()st vlexists()whether value label existsst vldrop()drop valuest vlmap()map valuesst vlsearch()map textst vlload()load value labelst vlmodify()create or modify value label Temporary variables&time-series operatorsst tempname()st tempname()temporary variable namest tempfilename()temporaryfilenamest tsrevar()st tsrevar()create time-series op.varnamest tsrevar()sameAdding&removing variables&observationsst addobs()st addobs()add observations to Stata dataset st addvar()st addvar()add variable to Stata datasetst addalias()st addalias()add alias to Stata datasetst addalias()samest dropvar()st dropvar()drop variablesst dropobsin()drop specified observationsst dropobsif()drop selected observationsst keepvar()keep variablesst keepobsin()keep specified observationsst keepobsif()keep selected observationsst updata()st updata()query/set data-have-changedflagStata—Stata interface functions3Executing Stata commandsstata()stata()execute Stata commandst macroexpand()st macroexpand()expand Stata macrosAccessing e(),r(),s(),macros,matrices,etc.st global()st global()obtain/set Stata globalst global hcat()obtain hidden/historical statusst local()st local()obtain/set local Stata macrost numscalar()st numscalar()obtain/set Stata numeric scalarst numscalar hcat()obtain hidden/historical statusst strscalar()obtain/set Stata string scalarst matrix()st matrix()obtain/set Stata matrixst matrix hcat()obtain hidden/historical statusst matrixrowstripe()obtain/set row labelsst matrixcolstripe()obtain/set column labelsst replacematrix()replace existing Stata matrixst dir()st dir()obtain list of Stata objectsst rclear()st rclear()clear r()st eclear()clear e()st sclear()clear s()Parsing&verificationst isname()st isname()whether valid Stata namest islmname()whether valid local macro namest isfmt()st isfmt()whether valid%fmtst isnumfmt()whether valid numeric%fmtst isstrfmt()whether valid string%fmtabbrev()abbrev()abbreviate stringsstrtoname()strtoname()translate strings to Stata names4Stata—Stata interface functionsData framesst frame*()st framecurrent()return or change current framest framecreate()make new framest framedrop()drop(eliminate)existing framest framedropabc()drop all but current framest framerename()rename framest framecopy()copy contents of one frame to anotherst framereset()reset to empty default framest frameexists()whether frame name already existsst framedir()obtain vector of existing frame names DescriptionThe above functions interface with Stata.Remarks and examples The following manual entries have to do with getting data from or putting data into Stata:[M-5]st data()Load copy of current Stata dataset[M-5]st view()Make matrix that is a view onto current Stata dataset[M-5]st store()Modify values stored in current Stata dataset[M-5]st nvar()Numbers of variables and observations In some cases,you mayfind yourself needing to translate variable names into variable indices and vice versa:[M-5]st varname()Obtain variable names from variable indices[M-5]st varindex()Obtain variable indices from variable names[M-5]st tsrevar()Create time-series op.varname variables The other functions mostly have to do with getting and putting Stata’s scalars,matrices,and returned results:[M-5]st local()Obtain strings from and put strings into Stata[M-5]st global()Obtain strings from and put strings into global macros[M-5]st numscalar()Obtain values from and put values into Stata scalars[M-5]st matrix()Obtain and put Stata matricesStata—Stata interface functions5 The stata()function,documented in[M-5]stata()Execute Stata commandallows you to cause Stata to execute a command that you construct in a string.ReferenceGould,W.W.2008.Mata Matters:Macros.Stata Journal8:401–412.Also see[M-4]Intro—Categorical guide to Mata functionsStata,Stata Press,and Mata are registered trademarks of StataCorp LLC.Stata andStata Press are registered trademarks with the World Intellectual Property Organization®of the United Nations.Other brand and product names are registered trademarks ortrademarks of their respective companies.Copyright c 1985–2023StataCorp LLC,College Station,TX,USA.All rights reserved.。
STATA基础教程
STATA基础教程STATA基础教程
stata基本命令
1.使⽤命令use "⽂件路径.dta"
例如： use “H:\resourses\2021数学建模培训\多元回归案例分析\数据.dta”
2.通过导航栏【⽂件-打开】打开所需数据
下图演⽰如何打开及查看数据表
3. 查看数据表信息：describe（简写des）
4. 把数据表列出来：list
5. 概要统计命令：summarize（简写sum)
利⽤这个命令，可以得到：
观测案例数(Obs)、变量的平均值(Mean)、标准差、最⼩值和最⼤值
Variable为变量
6.画散点命令： scatter y x
7.画拟合线命令：twoway( scatter y x)(lfit y x) 可简写为tw
8.最⼩⼆乘估计：reg y x
9.统计计算机：display,可以简写为dis [⽤于做最基础的运算]
10.⽣成新变量命令generate,可以简写为gen 新变量名=原变量名的某个运算
例如：gen z=x^2#会产⽣x^2的⼀列数
11.去除变量的命令：drop 某变量名
例如：drop z #z那⼀栏就不见了，被删除了
12.提取残差的命令：reg y x,紧跟第⼆条命令：predict e,res 边学习边补充~。
stata操作指南
计量经济学stata操作（实验课）第一章stata基本知识1、stata窗口介绍2、基本操作（1）窗口锁定：Edit-preferences-general preferences-windowing-lock splitter （2）数据导入（3）打开文件：use E:\example.dta,clear（4）日期数据导入：gen newvar=date(varname, “ymd”)format newvar %td 年度数据gen newvar=monthly(varname, “ym”)format newvar %tm 月度数据gen newvar=quarterly(varname, “yq”)format newvar %tq 季度数据（5）变量标签Label variable tc ` “total output” ’（6）审视数据describelist x1 x2list x1 x2 in 1/5list x1 x2 if q>=1000drop if q>=1000keep if q>=1000（6）考察变量的统计特征summarize x1su x1 if q>=10000su q,detailsutabulate x1correlate x1 x2 x3 x4 x5 x6（7）画图histogram x1, width(1000) frequencykdensity x1scatter x1 x2twoway (scatter x1 x2) (lfit x1 x2)twoway (scatter x1 x2) (qfit x1 x2)（8）生成新变量gen lnx1=log(x1)gen q2=q^2gen lnx1lnx2=lnx1*lnx2gen larg=(x1>=10000)rename larg largeg large=(q>=6000)replace large=(q>=6000)drop ln*（8）计算功能display log(2)（9）线性回归分析regress y1 x1 x2 x3 x4vce #显示估计系数的协方差矩阵reg y1 x1 x2 x3 x4,noc #不要常数项reg y1 x1 x2 x3 x4 if q>=6000reg y1 x1 x2 x3 x4 if largereg y1 x1 x2 x3 x4 if large==0reg y1 x1 x2 x3 x4 if ～largepredict yhatpredict e1,residualdisplay 1/_b[x1]test x1=1 # F检验，变量x1的系数等于1test (x1=1) (x2+x3+x4=1) # F联合假设检验test x1 x2 #系数显著性的联合检验testnl _b[x1]= _b[x2]^2（10）约束回归constraint def 1 x1+x2+x3=1cnsreg y1 x1 x2 x3 x4,c(1)cons def 2 x4=1cnsreg y1 x1 x2 x3 x4,c(1-2)（11）stata的日志File-log-begin-输入文件名log off 暂时关闭log on 恢复使用log close 彻底退出（12）stata命令库更新Update allhelp command第二章有关大样本ols的stata命令及实例（1）ols估计的稳健标准差reg y x1 x2 x3,robust（2）实例use example.dta,clearreg y1 x1 x2 x3 x4test x1=1reg y1 x1 x2 x3 x4,rtestnl _b[x1]=_b[x2]^2第三章最大似然估计法的stata命令及实例（1）最大似然估计help ml（2）LR检验lrtest #对面板数据中的异方差进行检验（3）正态分布检验sysuse auto #调用系统数据集auto.dtahist mpg,normalkdensity mpg,normalqnorm mpg*手工计算JB统计量sum mpg,detaildi (r(N)/6)*((r(skewness)^2)+[(1/4)*(r(kurtosis)-3)^2])di chi2tail(自由度，上一步计算值)*下载非官方程序ssc install jb6jb6 mpg*正态分布的三个检验sktest mpgswilk mpgsfrancia mpg*取对数后再检验gen lnmpg=log(mpg)kdensity lnmpg, normaljb6 lnmpgsktest lnmpg第四章处理异方差的stata命令及实例（1）画残差图rvfplotrvfplot varname*例题use example.dta,clearreg y x1 x2 x3 x4rvfplot # 与拟合值的散点图rvfplot x1 # 画残差与解释变量的散点图（2）怀特检验estat imtest,white*下载非官方软件ssc install whitetst（3）BP检验estat hettest #默认设置为使用拟合值estat hettest,rhs #使用方程右边的解释变量estat hettest [varlist] #指定使用某些解释变量estat hettest,iidestat hettest,rhs iidestat hettest [varlist],iid（4）WLSreg y x1 x2 x3 x4 [aw=1/var]*例题quietly reg y x1 x2 x3 x4predict e1,resgen e2=e1^2gen lne2=log(e2)reg lne2 x2,nocpredict lne2fgen e2f=exp(lne2f)reg y x1 x2 x3 x4 [aw=1/e2f]（5）stata命令的批处理（写程序）Window-do-file editor-new do-file#WLS for examplelog using E:\wls_example.smcl,replaceset more offuse E:\example.dta,clearreg y x1 x2 x3 x4predict e1,resgen e2=e1^2g lne2=log(e2)reg lne2 x2,nocpredict lne2fg e2f=exp(lne2f)*wls regressionreg y x1 x2 x3 x4 [aw=1/e2f]log closeexit第五章处理自相关的stata命令及实例（1）滞后算子/差分算子tsset yearl.l2.D.D2.LD.（2）画残差图scatter e1 l.e1ac e1pac e1（3）BG检验estat bgodfrey（默认p=1）estat bgodfrey,lags(p)estat bgodfrey,nomiss0（使用不添加0的BG检验）（4）Ljung-Box Q检验reg y x1 x2 x3 x4predict e1,residwntestq e1wntestq e1,lags(p)* wntestq指的是“white noise test Q”，因为白噪声没有自相关（5）DW检验做完OLS回归后，使用estat dwatson（6）HAC稳健标准差newey y x1 x2 x3 x4,lag(p)reg y x1 x2 x3 x4,cluster(varname)（7）处理一阶自相关的FGLSprais y x1 x2 x3 x4 （使用默认的PW估计方法）prais y x1 x2 x3 x4,corc （使用CO估计法）（8）实例use icecream.dta, cleartsset timegraph twoway connect consumption temp100 time, msymbol(circle) msymbol(triangle) reg consumption temp price incomepredict e1, resg e2=l.e1twoway (scatter e1 e2) (lfit e1 e2)ac e1pac e1estat bgodfreywntestq e1estat dwatsonnewey consumption temp price income, lag (3)prais consumption temp price income, corcprais consumption temp price income, nologreg consumption temp l.temp price incomeestat bgodfreyestat dwatson第六章模型设定与数据问题（1）解释变量的选择reg y x1 x2 x3estat ic*例题use icecream.dta, clearreg consumption temp price incomeestat icreg consumption temp l.temp price incomeestat ic（2）对函数形式的检验（reset检验）reg y x1 x2 x3estat ovtest （使用被解释变量的2、3、4次方作为非线性项）estat ovtest, rhs （使用解释变量的幂作为非线性项，ovtest-omitted variable test）*例题use nerlove.dta, clearreg lntc lnq lnpl lnpk lnpfestat ovtestg lnq2=lnq^2reg lntc lnq lnq2 lnpl lnpk lnpfestat ovtest（3）多重共线性estat vif*例题use nerlove.dta, clearreg lntc lnq lnpl lnpk lnpfestat vif（4）极端数据reg y x1 x2 x3predict lev, leverage （列出所有解释变量的lev值）gsort –levsum levlist lev in 1/3*例题use nerlove.dta, clearquietly reg lntc lnq lnpl lnpk lnpfpredict lev, leveragesum levgsort –levlist lev in 1/3（5）虚拟变量gen d=(year>=1978)tabulate province, generate (pr)reg y x1 x2 x3 pr2-pr30（6）经济结构变动的检验方法1：use consumption_china.dta, cleargraph twoway connect c y year, msymbol(circle) msymbol(triangle)reg c yreg c y if year<1992reg c y if year>=1992计算F统计量方法2：gen d=(year>1991)gen yd=y*dreg c y d ydtest d yd第七章工具变量法的stata命令及实例（1）2SLS的stata命令ivregress 2sls depvar [varlist1] (varlist2=instlist)如：ivregress 2sls y x1 (x2=z1 z2)ivregress 2sls y x1 (x2 x3=z1 z2 z3 z4) ,r firstestat firststage,all forcenonrobust （检验弱工具变量的命令）ivregress liml depvar [varlist 1] (varlist2=instlist)estat overid （过度识别检验的命令）*对解释变量内生性的检验（hausman test），缺点：不适合于异方差的情形reg y x1 x2estimates store olsivregress 2sls y x1 (x2=z1 z2)estimates store ivhausman iv ols, constant sigmamore*DWH检验estat endogenous*GMM的过度识别检验ivregress gmm y x1 (x2=z1 z2) （两步GMM）ivregress gmm y x1 (x2=z1 z2),igmm （迭代GMM）estat overid*使用异方差自相关稳健的标准差GMM命令ivregress gmm y x1 (x2=z1 z2), vce (hac nwest[#])（2）实例use grilic.dta,clearsumcorr iq sreg lw s expr tenure rns smsa,rreg lw s iq expr tenure rns smsa,rivregress 2sls lw s expr tenure rns smsa (iq=med kww mrt age),restat overidivregress 2sls lw s expr tenure rns smsa (iq=med kww),r firstestat overidestat firststage, all forcenonrobust （检验工具变量与内生变量的相关性）ivregress liml lw s expr tenure rns smsa (iq=med kww),r*内生解释变量检验quietly reg lw s iq expr tenure rns smsaestimates store olsquietly ivregress 2sls lw s expr tenure rns smsa (iq=med kww)estimates store ivhausman iv ols, constant sigmamoreestat endogenous （存在异方差的情形）*存在异方差情形下，GMM比2sls更有效率ivregress gmm lw s expr tenure rns smsa (iq=med kww)estat overidivregress gmm lw s expr tenure rns smsa (iq=med kww),igmm*将各种估计方法的结果存储在一张表中quietly ivregress gmm lw s expr tenure rns smsa (iq=med kww)estimates store gmmquietly ivregress gmm lw s expr tenure rns smsa (iq=med kww),igmmestimates store igmmestimates table gmm igmm第八章短面板的stata命令及实例（1）面板数据的设定xtset panelvar timevarencode country,gen(cntry) （将字符型变量转化为数字型变量）xtdesxtsumxttab varnamextline varname,overlay*实例use traffic.dta,clearxtset state yearxtdesxtsum fatal beertax unrate state yearxtline fatal（2）混合回归reg y x1 x2 x3,vce(cluster id)如：reg fatal beertax unrate perinck,vce(cluster state)estimates store ols对比：reg fatal beertax unrate perinck（3）固定效应xtreg y x1 x2 x3,fe vce(cluster id)xi:reg y x1 x2 x3 i.id,vce(cluster id) （LSDV法）xtserial y x1 x2 x3,output （一阶差分法，同时报告面板一阶自相关）estimates store FD*双向固定效应模型tab year, gen (year)xtreg fatal beertax unrate perinck year2-year7, fe vce (cluster state)estimates store FE_TWtest year2 year3 year4 year5 year6 year7（4）随机效应xtreg y x1 x2 x3,re vce(cluster id) （随机效应FGLS）xtreg y x1 x2 x3,mle （随机效应MLE）xttest0 （在执行命令xtreg, re 后执行，进行LM检验）（5）组间估计量xtreg y x1 x2 x3,be（6）固定效应还是随机效应：hausman testxtreg y x1 x2 x3,feestimates store fextreg y x1 x2 x3,reestimates store rehausman fe re,constant sigmamore （若使用了vce(cluster id)，则无法直接使用该命令，解决办法详见P163）estimates table ols fe_robust fe_tw re be, b se （将主要回归结果列表比较）第九章长面板与动态面板（1）仅解决组内自相关的FGLSxtpcse y x1 x2 x3 ,corr(ar1) （具有共同的自相关系数）xtpcse y x1 x2 x3 ,corr(psar1) （允许每个面板个体有自身的相关系数）例题：use mus08cigar.dta,cleartab state,gen(state)gen t=year-62reg lnc lnp lnpmin lny state2-state10 t,vce(cluster state)estimates store OLSxtpcse lnc lnp lnpmin lny state2-state10 t,corr(ar1) (考虑存在组内自相关，且各组回归系数相同)estimates store AR1xtpcse lnc lnp lnpmin lny state2-state10 t,corr(psar1) (考虑存在组内自相关，且各组回归系数不相同)estimates store PSAR1xtpcse lnc lnp lnpmin lny state2-state10 t, hetonly (仅考虑不同个体扰动性存在异方差，忽略自相关)estimates store HETONL Yestimates table OLS AR1 PSAR1 HETONL Y, b se（2）同时处理组内自相关与组间同期相关的FGLSxtgls y x1 x2 x3,panels (option/iid/het/cor) corr(option/ar1/psar1) igls注：执行上述xtpcse、xtgls命令时，如果没有个体虚拟变量，则为随机效应模型；如果加上个体虚拟变量，则为固定效应模型。
Stata数据分析软件用户指南说明书
16.1.6 Preventing –more– conditions
Stata has more turned off by default; see [U] 7 –more– conditions.
If you have set more on for interactive use, Stata’s feature of pausing every time the screen is full will probably be an irritation when you are running a do-file and logging the output.
6. You can run the do-file in batch mode. See [GSM] B.3 Stata batch mode for details, but the short explanation is that you open a Terminal window and type
a. Stata automatically opens a log. If you specified /s, Stata will open a SMCL log; if you specified /b, Stata will open a plain text log. If your do-file is named xyz.do, the log will be called xyz.smcl (/s) or xyz.log (/b) in the same directory.
8 [ U ] 16 Do-files
Many users include the commands to start and stop the logging in the do-file itself:
最全Stata面板数据学习手册
最全Stata面板数据学习手册来源：本文授权转载自数量经济学本文包括静态与动态面板数据处理方法，包含hausman检验，固定效应检验，随机效应检验，异方差检验、相关检验，面板logit与面板probit模型、面板泊松模型、面板负二项模型等众多干货内容，欢迎阅读。
本文目录一、静态面板数据●数据处理●模型的筛选和检验1、检验个体效应（混合效应还是固定效应）2、检验时间效应（混合效应还是随机效应）3、检验固定效应模型or随机效应模型（检验方法：Hausman 检验）●模型的筛选和检验1、固定效应估计2、随机效应估计省略3、时间固定效应（以上分析主要针对的是个体效应）●异方差和自相关检验1、异方差检验（组间异方差）2、序列相关检验3、“异方差—序列相关”稳健型标准误4、截面相关检验5、“异方差—序列相关—截面相关”稳健型标准误二、动态面板数据三、面板logit与面板probit模型四、面板泊松模型五、面板负二项模型六、面板Tobit模型七、面板工具变量法八、面板随机前沿模型一．静态面板数据的STATA处理命令（一）数据处理输入数据use 'E:\stata\data\FDI.dta', cleartsset code year 该命令是将数据定义为“面板”形式xtdes 该命令是了解面板数据结构summarize lngdp lnfdi lnie lnex lnim lnci lngp各变量的描述性统计（统计分析）拓展命令：gen lag_y=L.y 产生一个滞后一期的新变量gen F_y=F.y 产生一个超前项的新变量gen D_y=D.y 产生一个一阶差分的新变量gen D2_y=D2.y 产生一个二阶差分的新变量（二）模型的筛选和检验1、检验个体效应（混合效应还是固定效应）（原假设：使用OLS 混合模型）xtreg lngdp lnfdi lnie lnex lnim lnci lngp,fe对于固定效应模型而言，回归结果中最后一行汇报的F统计量便在于检验所有的个体效应整体上显著。
stata使用手册
STATA基本入门前言STATA是一个十分好用而且简单的统计软件包，透过轻松的数据输入方式，而且简单的指令，即可执行一般在计量经济学上常用的计量模型。
除了计量模型外，STATA的软件包中也可执行统计学中的估计和检定，甚至是多变量分析中的各项分析工具。
因此，STATA可以说是一个相当强而有力的统计软件。
一、安装STATA所须的内存容量不大，只有4.03MB。
此外，安装也相当简单，只要在〝SETUP〞上点两下，安装完成后再分别输入”Sn”、”Code”和”Key”即可开始使用。
但是安装过程中有一点必须注意的是，如下图所示，有”Intercooled”和”Small”两个选项。
一般而言，为了方便日后要设定较大的内存容量来处理大笔的资料，通常选择以”Intercooled”进行安装。
以此項進行安裝二、窗口介绍安装完成后，点选桌面上STATA的图标，窗口画面如下图所示。
为了使画面美观，我们可以将画面拉到自己喜欢的地方，如下图所示。
为了保存这个窗口画面，我们必须点选工具列上的”Prefs”下的”Save Windowing Preferences”。
如此一来，以后开启STATA时都会以此窗口画面呈现。
接下来，我们依序介绍四个窗口的功用：左上─Review：此一窗口用于记录在开启STATA后所执行过的所有指令。
因此，若欲使用重复的指令时，只要在该指令上点选两下即可执行相同的指令；若欲使用类似的指令时，在该指令上点一下，该指令即会出现在窗口”Stata Command”上，再进行修改即可。
此外，STATA还可以将执行过的指令储存下来，存在一个do-file内，下次即可再执行相同的指令。
左下─Variables：此一窗口用于呈现某笔数据中的所有变量。
换言之，当数据中的变量都有其名称时，变量名称将会出现在此一窗口中。
只要数据有读进STATA中，变量名称就会出现。
它的优点是(1)确认数据输入无误；(2)只要在某变量上点选两下，该变量即会出现在窗口”Stata Command”上。
Stata数据分析软件用户指南.pdf_1704610398.5038798说明书
Title Intro5—Other tabulation commandsDescription Remarks and examples Also seeDescriptionTables can be produced by a few other commands.Remarks and examples The table command is not considered an“other”table command.It is not listed below.Although table is documented in[R],it is part of the collection system.It is actually implemented on top of the collection system documented in this manual.table leaves behind a collection that can be manipulated using all the tools in this manual.Other tabulation commands are the following:Command Descriptiontabulate(oneway)One-way tabulations with percentages and cumulative percentages tabulate(twoway)Two-way tabulations.Optionally computes statistics forindependence of the rows and columnstabulate,summarize()One-way or two-way tabulations of summary statisticstabstat Tables of summary statisticssvy:tabulate(oneway)Survey version of tabulate(oneway)svy:tabulate(twoway)Survey version of tabulate(twoway)Aside from the independence statistics computed by two-way tabulate and two-way svy:tabu-late and the cumulative percentages computed by(one-way)tabulate,all of these commands have been superseded by[R]table.The independence statistics are returned in r()and can be collected.The svy versions of tabulate also have options that return MEFF,DEFF,and other survey statistics.Those results are returned in e()and can be collected.Aside from the independence statistics and survey statistics,none of these commands returns results,and thus,their tabulations cannot be used in collections.That said,it is often useful to collect independence statistics and include them in tables created from collections.To be fully truthful,tabstat with the save option will store results into r().You would never collect these results because table can compute all of the statistics that tabstat can,and more.More importantly,table automatically puts all the statistics it computes into a collection that is easy to work with.These commands are all are still documented primarily because they provide an easy and familiar way to quickly analyze your data.That is,the data and the independence statistics.Also see[TABLES]Intro4—Overview of commands12Intro5—Other tabulation commandsStata,Stata Press,and Mata are registered trademarks of StataCorp LLC.Stata andStata Press are registered trademarks with the World Intellectual Property Organization®of the United Nations.Other brand and product names are registered trademarks ortrademarks of their respective companies.Copyright c 1985–2023StataCorp LLC,College Station,TX,USA.All rights reserved.。
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STATA基本入门前言STATA是一个十分好用而且简单的统计软件包，透过轻松的数据输入方式，而且简单的指令，即可执行一般在计量经济学上常用的计量模型。
除了计量模型外，STATA的软件包中也可执行统计学中的估计和检定，甚至是多变量分析中的各项分析工具。
因此，STATA可以说是一个相当强而有力的统计软件。
一、安装STATA所须的内存容量不大，只有4.03MB。
此外，安装也相当简单，只要在〝SETUP〞上点两下，安装完成后再分别输入”Sn”、”Code”和”Key”即可开始使用。
但是安装过程中有一点必须注意的是，如下图所示，有”Intercooled”和”Small”两个选项。
一般而言，为了方便日后要设定较大的内存容量来处理大笔的资料，通常选择以”Intercooled”进行安装。
以此項進行安裝二、窗口介绍安装完成后，点选桌面上STATA的图标，窗口画面如下图所示。
为了使画面美观，我们可以将画面拉到自己喜欢的地方，如下图所示。
为了保存这个窗口画面，我们必须点选工具列上的”Prefs”下的”Save Windowing Preferences”。
如此一来，以后开启STATA时都会以此窗口画面呈现。
接下来，我们依序介绍四个窗口的功用：左上─Review：此一窗口用于记录在开启STATA后所执行过的所有指令。
因此，若欲使用重复的指令时，只要在该指令上点选两下即可执行相同的指令；若欲使用类似的指令时，在该指令上点一下，该指令即会出现在窗口”Stata Command”上，再进行修改即可。
此外，STATA还可以将执行过的指令储存下来，存在一个do-file内，下次即可再执行相同的指令。
左下─Variables：此一窗口用于呈现某笔数据中的所有变量。
换言之，当数据中的变量都有其名称时，变量名称将会出现在此一窗口中。
只要数据有读进STATA中，变量名称就会出现。
它的优点是(1)确认数据输入无误；(2)只要在某变量上点选两下，该变量即会出现在窗口”Stata Command”上。
右上─Stata Results：此一窗口用于呈现并记录指令执行后的结果。
右下─Stata Command：此一窗口用于输入所欲执行的指令。
Note：以上四个窗口都可以从”Fonts”去更改字体大小。
三、输入数据（Entering data）在本小节中，我们将介绍如何把数据读进STATA。
但是在正式介绍之前，我们必须先对几个一般性的指令（general command）有所了解，说明如下：cd：即change directory，简言之，告知STATA数据储存的地方。
例如当数据储存在e槽的sample数据夹时，则必须先输入cd e:\sample。
dir/ls：用来显示目录的内容。
set memory#m：设定内存的容量。
例如：当有一笔庞大的数据要处理时，则可设定100mb的容量，此时可输入set memory100m。
（输入指令memory可以知道内存容量的大小以及使用情况。
）set matsize#：设定所需的变量个数。
一般而言，不须对此部分进行设定，除非所欲处理的资料庞大或是当执行后出现matsize toosmall的讯息时再进行修改即可。
内建为40。
set more off/on：若欲执行结果以分页的型式呈现时，则输入set moreon；若欲执行结果同时呈现时，则输入set more off。
help：求助键。
后面必须接的是指令。
说明如何使用该指令，例如：help regress。
search：求助键。
后面可接任何文字。
说明在何处可以找到该文字。
例如：search normal distribution。
clear：清除键。
用来删除所有数据。
接下来，根据数据类型或指令的不同，数据输入的方法可分成以下四种：1、输入EXCEL数据将EXCEL的数据输入STATA的方式还可细分成以下两种：①将EXCEL的数据输入STATA之前，必须先将数据存成csv文件，再利用指令insheet来读数据。
Example：❶当csv档的第一列有变量名称时：❷当csv档的第一列没有变量名称时：②直接复制EXCEL上的数据，再到STATA选取”Window”下的”Data Editor”，点选后会出现”Stata Editor”工作表，再到”Edit”下选取”Paste”即可贴上数据。
2、输入ASCII的数据型态依ASCII的数据型态区分，将ASCII的数据输入STATA的方式也有以下两种：①数据型态一：见sample1-3.txtNote：记住文字的设定方式（str#variable name）。
②数据型态二：见sample1-4.txt第二种的数据型态通常须要codebook。
如下表所示。
3、利用Do-file editor输入数据将数据或是指令写入Do-file editor，再执行即可。
例如：将下面数据复制并贴在Do-file editor（选取”Window”下的”Do-file editor”）上，再选择”do currnet file”执行即可。
4、利用STATA的数据型态输入除了以上三种方法之外，还可以开启之前以STATA储存的资料。
Note：此一指令亦可用在读取网络上的数据（use网址）。
最后，将数据输入的相关指令整理成下表。
四、探索资料（Exploring data）为了更详细地呈现出在数据探索时所需使用的相关指令，我们利用sample4-1来说明指令的用法。
首先，利用前节所提及的数据输入方法将sample4-1读进STATA。
在正式分析数据之前，我们可以利用一个log档来储存之后所要执行的指令以及所得到的结果。
指令的表示方法如下：接下来，我们可以先利用下面的指令来检视sample4-1的数据：count：可得样本数。
describe：描述数据来源以及数据大小。
list：依序列出观察值的各个变量值。
codebook：描述资料的详细内容。
此外，我们就可以利用summarize、tabulate和tabstat等指令得到数据的叙述统计与基本特性。
表示如下：summarize：列出资料的叙述统计。
Example：summarize write,detailsum write if read>=60（sum是summarize的简写）sum write if prgtype=="academic"（接在if之后的句子中的”=”要放两个）sum write in1/40（只列出第1笔到第40笔资料）tabulate：列出变数的次数表。
Example：tabulate prgtypetabulate prgtype racetabulate prgtype,summarize(read)tabulate prgtype race,summarize(write)tabstat：列出变量的叙述统计。
Example：tabstat read write math,by(prgtype)stat(n mean sd)tabstat write,stat(n mean sd p25p50p75)by(prgtype)接下来，我们介绍一些用来划图的指令：茎叶图：stem writestem write,lines(2)直方图：graph write,bin(10)graph write,hist normal bin(10)箱形图：graph write,boxsort prgtype（要先有这个指令才能执行下一个指令）graph write,box by(prgtype)此外，利用correlate或是pwcorr可以得到相关矩阵；亦可利用graph 划出散布图。
现在我们可以将log文件结束了，指令输入如下：若欲检视log档中的结果，可以输入指令：或是到所储存的目录下点选。
最后，将数据探索的相关指令整理成下表。
五、修饰资料（Modifying data）在本小节中，我们亦利用sample4-1的数据进行说明。
首先，读进数据。
读完数据后，可以为此数据取个名称，指令如下：现在我们可以将变量的顺序作一排列。
例如：原先的变量顺序为gender、id和race…，但是我们想把顺序改成id、gender和race…，则可以下面的指令来执行：在执行codebook时，我们会发现有些变量尚未加上卷标（label），为了更清楚地表达变量所代表的意义，我们可以执行以下的指令：现在，我们想要产生一个新变量total，此变量代表read、write和math 的总和。
指令如下：此外，若是我们想加总的分数是read、write和socst，而非read、write和math，此时的指令输入如下：另一方面，我们还可以将变量total表示成以等级（A、B、C、D and F）的形式。
指令如下：为了记忆变量的意义为何，我们还可以利用note的方式来记录变量。
指令如下：另外，介绍一些利用公式来产生变量的指令。
最后，我们可以将以上的执行结果储存下来。
指令如下：现在亦将数据修饰的相关指令整理成下表。
六、管理数据（Managing data）在本节中，我们将进一步介绍如何将数据作一些特殊的处理，例如：保留所欲分析的数据、删除多余的数据或是将两份数据结合等等。
假设我们只想针对部分的数据进行处理，而又想保留原始资料时，则有以下两种方法可进行：1、另存新檔：亦即将所欲分析的部分数据储存在另一个档案中。
例如：我们只针对read成绩大于或是等于60分的学生进行分析，则可利用下面的指令来筛选。
Note：当只要保留某些变量时，则利用指令keep。
例如：keep read write。
2、直接处理：亦即在原始数据上进行分析。
承上例，指令输入如下：Note：若要删除某些变量时，则利用指令drop。
例如：drop read write。
接下来，我们介绍如何将两笔数据结合在一起。
数据的结合主要可以分为两种，水平合并和垂直合并。
前者是指变量的增加；后者则是指样本数的增加。
说明如下：1、水平合并2、垂直合并：Note：在垂直合并前要记得先sort。
最后，我们将数据修饰的相关指令整理成下表。
七、资料分析透过前面几节的介绍，应该对于STATA的指令和使用方法有了基本的认识。
现在，我们开始说明如何利用STATA来处现统计上的问题以及计量方面的模型。
1、检定：我们利用下面的例子来示范如何进行统计上的检定工作。
2、回归在执行回归分析时所使用的指令为regress。
另外，当存在heterogeneity of variance的问题时，可在后面加上robust；另外，若是不想放入截距项时，可在后面加上noconstant。
若欲得到残差值，可输入以下指令：123、二元选择模型在执行二元选择模型时所使用的程序写法与执行回归分析时相同，只是所使用的指令不同。