Review_Stata_Application

4Getting helpSystem helpStata’s help system provides a wealth of information to help you learn and use Stata.Tofind out which Stata command will perform the statistical or data management task you would like to do,you should generally follow these steps:1.Select Help>Search...,choose Search all,and enter the topic or keywords.This searchwill open a new Viewer window containing information about Stata commands,references to articles in the Stata Journal,links to Frequently Asked Questions(FAQ s)on Stata’s website, links to videos on Stata’s YouTube channel,links to selected external websites,and links to community-contributed features.2.Read through the results.If youfind a useful command,click on the link to the appropriatecommand name to open its helpfile.3.Read the helpfile for the command you chose.4.If you want more in-depth help,click on the link from the name of the command to the PDFdocumentation,read it,then come back to Stata.5.If thefirst helpfile you went to is not what you wanted,either click on the Also see menuand choose a link to related helpfiles or click on the Back button to go back to the previous document and go from there to other helpfiles.6.With the helpfile open,click on the Command window and enter the command,or click onthe Dialog button and choose a link to open a dialog for the command.7.If,at any time,you want to begin again with a new search,enter the new search terms in thesearch box of the Viewer window.8.If you select Search documentation and FAQs,Stata searches its keyword database for officialStata commands,Stata Journal articles and software,FAQ s,and videos.If you select Search net resources,Stata searches for community-contributed commands,whether they are from the Stata Journal or elsewhere;see[GSM]19Updating and extending Stata—Internet functionality for more information.Let’s illustrate the help system with an example.You will get the most benefit from the example if you work along at your computer.Suppose that we have been given a dataset about antique cars and that we need to know what it contains.Though we still have a vague notion of having seen something like this while working through the example session in[GSM]1Introducing Stata—sample session,we do not remember the proper command.Start by typing sysuse auto,clear in the Command window to bring the dataset into memory.(See[GSM]5Opening and saving Stata datasets for information on the clear option.) Follow the above approach:1.Select Help>Search....2.Check that the Search all radio button is selected.3.Type dataset contents into the search box and click on OK or press Return.Before wepress Return,the window should look like12[GSM]4Getting help4.Stata will now search for“dataset contents”among the Stata commands,the reference manuals,the Stata Journal,the FAQ s on Stata’s website,and community-contributed features.Here is the result:[GSM]4Getting help3 5.Upon seeing the results of the search,we see two commands that look promising:codebookand describe.Because we are interested in the contents of the dataset,we decide to check out the codebook command.The[D]means that we could look up the codebook command in the Data Management Reference Manual.The codebook link in(help codebook)means that there is a system helpfile for the codebook command.This is what we are interested in right now.6.Click on the codebook link.Links can take you to a variety of resources,such as help for Statacommands,dialogs,and even webpages.Here the link goes to the helpfile for the codebook command.7.What is displayed is typical for help for a Stata command.Helpfiles for Stata commandscontain,from top to bottom,these features:a.The quick access toolbar with three buttons:i.The Dialog button shows links to any dialogs associated with the command.ii.The Also see button shows links to related PDF documentation and helpfiles.iii.The Jump to button shows links to other sections within the current helpfile.b.The second line of a helpfile shows a View complete PDF manual entry link.Clicking on the link will open the complete documentation for the command—in this case,codebook—in your PDF viewer.c.The command’s syntax,that is,rules for constructing a command that Stata will correctlyinterpret.The square brackets here indicate that all the arguments to codebook are optional but that if we wanted to specify them,we could use a varlist,an if qualifier,or an in qualifier,along with some options.(Options vary greatly from command to command.) The options are listed directly under the command and are explained in some detail later in the helpfile.You will learn more about command syntax in[GSM]10Listing data and basic command syntax.4[GSM]4Getting helpd.A description of the command.Because“codebook”is the name for big binders containinga hard copy describing each of the elements of a dataset,the description for the codebookcommand is justifiably terse.e.The options that can be used with this command.These are explained in much greaterdetail than in the listing of the possible options after the syntax.Here,for example,we cansee that the mv option can look to see if there is a pattern in the missing values—somethingimportant for data cleaning and imputation.f.Examples of command usage.The codebook examples are real examples that step throughusing the command on a dataset either shipped with Stata or loadable within Stata fromthe Internet.g.The information the command stores in the returned results.These results are usedprimarily by programmers.For now,either click on Jump to and choose Examples from the drop-down menu or scroll down to the examples.It is worth going through the examples as given in the helpfile.Here is a screenshot of the top of the examples:[GSM]4Getting help5 Searching helpSearch is designed to help youfind information about statistics,graphics,data management,and programming features in Stata,either as part of the official release or as community-contributed features.When entering topics for the search,use appropriate terms from statistics,etc.For example,you could enter Mann-Whitney.Multiple topic words are allowed,for example,regression residuals.When you are using Search,use proper English and proper statistical terminology.If you already know the name of the Stata command and want to go directly to its helpfile,select Help>Stata command...and type the command name.You can also type the command name in the Searchfield at the top of the Viewer and press Return.Help distinguishes between topics and Stata commands because some names of Stata commands are also general topic names.For example,logistic is a Stata command.If you choose Stata command...and type logistic,you will go right to the helpfile for the command.But if you choose Search...and type logistic,you will get search results listing the many Stata commands that relate to logistic regression.Remember that you can search for help from within a Viewer window by typing a command in the command box of the Viewer or by clicking the magnifying glass button to the left of the search box,selecting the scope of your search,typing the search criteria in the search box,and pressing Return.Help and search commandsAs you might expect,the help system is accessible from the Command window.This feature is especially convenient when you need help on a particular Stata command.Here is a short listing of the various commands you can use:•Typing help commandname is equivalent to selecting Help>Stata command...and typing commandname.The helpfile for the command appears in a new Viewer tab.•Typing search topic in the Command window produces the same output as selecting Help> Search...,choosing Search all,and typing topic.The output appears in a new Viewer tab.•Typing search topic,local in the Command window produces the same output as selecting Help>Search...,choosing Search documentation and FAQs,and typing topic.The output appears in the Results window instead of a Viewer.•Typing search topic,net in the Command window produces the same output as selecting Help>Search...,choosing Search net resources,and typing topic.The output appears in the Results window instead of a Viewer.See[U]4Stata’s help and search facilities and[U]4.8search:All the details in the User’s Guide for more information about these command-language versions of the help system.The search command,in particular,has a few capabilities(such as author searches)that we have not demonstrated here.The Stata reference manuals and User’s GuideAll the Stata reference manuals come as PDFfiles and are included with the software.The manuals themselves have many cross-references in the form of clickable links,so you can easily read the documentation in a nonlinear way.Many of the links in the helpfiles point to the PDF manuals that came with Stata.It is worth clicking on these links to read the extensive information found in the manuals.The Stata help system, though extensive,contains only a fraction of the information found in the manuals.6[GSM]4Getting helpMost Stata reference manuals are each arranged alphabetically.Each Getting Started with Stata has its own index.A combined index for all other manuals can be found in the Stata Index.This combined index is a good place to start when you are looking for information about a command.Entries have names like collapse,egen,and summarize,which are generally themselves Stata commands.Notations such as[R]ci,[R]regress,and[R]ttest in the Search results and helpfiles are references to the Base Reference Manual.You may also see things like[P]PyStata integration,which is a reference to the Programming Reference Manual,and[U]12Data,which is a reference to the User’s Guide.For a complete list of manuals and their shorthand notations,see Cross-referencing the documentation,which immediately follows the table of contents in this manual.For advice on how to use the reference manuals,see[GSM]18Learning more about Stata,or see[U]1.2The User’s Guide and the Reference manuals.Stata videosThe Stata YouTube channel is an excellent resource for learning about Stata.The brief videos demonstrate many topics using Stata’s graphical user interface.They cover basic topics,such as data management,graphics,summary statistics,and hypothesis testing,and advanced topics,such as multilevel models and structural equation models.There are also several playlists that provide a series of videos about a topic in sequence.For example,the“Power and sample size calculations”playlist includes videos about how to calculate power,sample size,and effect size for two independent proportions and for paired samples.The “Survival analysis”playlist takes you through the process of setting your data up for survival analysis, conducting basic descriptive analysis of survival data,graphing survival data,and calculating survivor functions and life tables.The“Time series”playlist takes you through the process of setting your data up for time-series analysis,creating time-series graphs,using time-series operators in estimation, andfitting ARMA and ARIMA models.There is even a“Back-to-school video”playlist for students who are using Stata for thefirst time or want a refresher after summer break.See https:///links/video-tutorials/for an up-to-date list of videos organized by topic.The playlists can be accessed directly at https:///user/statacorp/.The Stata JournalWhen searching in Stata,you will often see links to the Stata Journal.The Stata Journal is a printed and electronic journal,published quarterly,containing articles about statistics,data analysis,teaching methods,and effective use of Stata’s language.The Journal publishes peer-reviewed papers together with shorter notes and comments,regular columns,tips,book reviews, and other material of interest to researchers applying statistics in a variety of disciplines.The Journal is a publication for all Stata users,both novice and experienced,with different levels of expertise in statistics,research design,data management,graphics,reporting of results,and Stata,in particular.See https:// for more information.Associated with each issue of the Stata Journal are the programs and datasets described therein.These programs and datasets are made available for download and installation over the Internet,not only to subscribers but also to all Stata users.See[R]net and[R]sj for more information.Because the Stata Journal has had several articles about measures of inequality,if you select Help >Search...,choose Search documentation and FAQs,type inequality,and scroll down a bit,you will see some of these references:[GSM]4Getting help7SJ-18-3refers to volume18,number3of the Stata Journal.st0539refers to the package name; st indicates that this package is in the“statistics”category of the Stata Journal.Listed next is the title of the software package and the authors.The community-contributed commands found within this package are listed next in parentheses,followed by the reference details of the article.Clicking on an SJ link,such as SJ18(3):692--715,will open a browser and take you to the Stata Journal website,where you can view the abstract of the article and purchase the article.The search listing concludes with a brief description of the community-contributed package.The Stata Journal website allows all articles older than three years to be downloaded for free. See Downloading community-contributed commands in[GSM]19Updating and extending Stata—Internet functionality for more details on how to install community-contributed software.Also see [R]ssc for information on a convenient interface to resources available from the Statistical Software Components(SSC)Archive.We recommend that all users subscribe to the Stata Journal.See[U]3.4The Stata Journal for more information.Links to other sites where you can freely download programs and datasets for Stata can be found on the Stata website;see https:///links/.8[GSM]4Getting helpStata,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.。

三、輸入資料（Entering data）
在本小節中，我們將介紹如何把資料讀進 STATA。但是在正式介紹之前， 我們必須先對幾個一般性的指令（general command）有所瞭解，說明如下： cd：即 change directory，簡言之，告知 STATA 資料儲存的地方。例如當 資料儲存在 e 槽的 sample 資料夾時，則必須先輸入 cd e:\sample。 dir/ls：用來顯示目錄的內容。 set memory #m：設定記憶體的容量。例如：當有一筆龐大的資料要處理 時，則可設定 100mb 的容量，此時可輸入 set memory 100m 。 （輸入指令 memory 可以知道記憶體容量的大小 以及使用情況。） set matsize #：設定所需的變數個數。一般而言，不須對此部分進行設定， 除非所欲處理的資料龐大或是當執行後出現 matsize too small 的訊息時再進行修改即可。內建為 40。 set more off/on：若欲執行結果以分頁的型式呈現時，則輸入 set more on； 若欲執行結果同時呈現時，則輸入 set more off。 help：求助鍵。後面必須接的是指令。說明如何使用該指令，例如：help
5
use sample1-6.dta Note：此一指令亦可用在讀取網路上的資料（use 網址）。 最後，將資料輸入的相關指令整理成下表。 insheet read ASCII (text) data created by a spreadsheet infile infix input use read unformatted ASCII (text) data read ASCII (text) data in fixed format enter data from keyboard load a Stata-format dataset

Useful Stata Commands (for Stata version 10)Kenneth L. Simons– This document is updated continually. For the latest version, open it from the course disk space. – This document briefly summarizes Stata commands useful in ECON-4570 Econometrics and ECON-6570 Advanced Econometrics.This presumes a basic working knowledge of how to open Stata, use the menus, use the data editor, and use the do-file editor. We will cover these topics in early Stata sessions in class. If you miss the sessions, I suggest you ask fellow students to show you through basic usage of Stata, and get the recommended text about Stata for the course and use it to practice with Stata.More replete information is available in three very useful books: Lawrence C. Hamilton’s Statistics with Stata updated for version 9(recommended in past for ECON-4570 Econometrics), Christopher F. Baum’s An Introduction to Modern Econometrics Using Stata(recommended for ECON-6570 Advanced Econometrics), and A. Colin Cameron and Pravin K. Trivedi’s Microeconometrics using Stata (just released and is great and the most thorough of the three).Throughout, estimation commands specify robust standard errors (Eicker-Huber-White heteroskedastic-consistent standard errors). This does not imply that robust rather than conventional estimates of Var[b|X] should always be used, nor that they are sufficient. Other estimators shown here include Davidson and MacKinnon’s improved small-sample robust estimators for OLS, cluster-robust estimators useful when errors may be arbitrarily correlated within groups (one application is across time for an individual), and the Newey-West estimator to allow for time series correlation of errors. Selected GLS estimators are listed as well. Hopefully the constant presence of “vce(robust)” in estimation commands will make readers sensitive to the need to account for heteroskedasticity and other properties of errors typical in real data and models.ContentsPreliminaries for RPI Dot.CIO Labs...........................................................................................................4
A. Loading Data..........................................................................................................................................4
B. Variable Lists, If-Statements, and Options............................................................................................4
C. Lowercase and Uppercase Letters..........................................................................................................5
D. Review Window, and Abbreviating Command Names.........................................................................5
E. Viewing and Summarizing Data............................................................................................................5
E1. Just Looking.....................................................................................................................................5
E2. Mean, Variance, Number of Non-missing Observations, Minimum, Maximum, Etc.....................5
E3. Tabulations, Histograms, Density Function Estimates.....................................................................5
E4. Scatter Plots and Other Plots............................................................................................................6
E5. Correlations and Covariances...........................................................................................................6
F. Generating and Changing Variables.......................................................................................................6
F1. Generating Variables........................................................................................................................6
F2. True-False Variables.........................................................................................................................7
F3. Random Numbers.............................................................................................................................7
F4. Replacing Values of Variables.........................................................................................................7
F5. Getting Rid of Variables...................................................................................................................7
F6. If-then-else Formulas........................................................................................................................8
F7. Quick Calculations............................................................................................................................8
F8. More..................................................................................................................................................8
G. Means: Hypothesis Tests and Confidence Intervals..............................................................................8
G1. Confidence Intervals........................................................................................................................8
G2. Hypothesis Tests..............................................................................................................................8
H. OLS Regression (and WLS and GLS)...................................................................................................9
H1. Improved Robust Standard Errors in Finite Samples.......................................................................9
H2. Weighted Least Squares...................................................................................................................9
H3. Feasible Generalized Least Squares...............................................................................................10
I. Post-Estimation Commands..................................................................................................................10
I1. Fitted Values, Residuals, and Related Plots....................................................................................10
I2. Confidence Intervals and Hypothesis Tests.....................................................................................10
I3. Nonlinear Hypothesis Tests.............................................................................................................11
I4. Computing Estimated Expected Values for the Dependent Variable..............................................11
I5. Displaying Adjusted R2 and Other Estimation Results...................................................................12
I6. Plotting Any Mathematical Function..............................................................................................12
I7. Influence Statistics...........................................................................................................................12
I8. Functional Form Test.......................................................................................................................13
I9. Heteroskedasticity Tests..................................................................................................................13
I10. Serial Correlation Tests.................................................................................................................13
I11. Variance Inflation Factors.............................................................................................................14
J. Tables of Regression Results................................................................................................................14
J1. Tables of Regression Results Using Stata’s Built-In Commands...................................................14
J2. Tables of Regression Results Using Add-On Commands...............................................................15
J2a. Installing or Accessing the Add-On Commands.......................................................................15
J2b. Storing Results and Making Tables...........................................................................................16
J2c. Understanding the Table Command’s Options.........................................................................16
J2d. Wide Tables...............................................................................................................................16
J2e. Storing Additional Results.........................................................................................................17
J2f. Saving Tables as Files................................................................................................................17
J2g. Clearing Stored Results.............................................................................................................17
J2h. More Options and Related Commands.....................................................................................17
K. Data Types, When 3.3 ≠ 3.3, and Missing Values...............................................................................17
L. Results Returned after Commands.......................................................................................................18
M. Do-Files and Programs........................................................................................................................18
N. Monte-Carlo Simulations.....................................................................................................................19
O. Doing Things Once for Each Group....................................................................................................20
P. Generating Variables for Time-Series and Panel Data.........................................................................20
P1. Creating Time Variables that Start from a First Time and Increase by 1 at Each Observation.....20
P2. Creating Time Variables Using a String Date................................................................................21
P3. Telling Stata You Have Time Series or Panel Data.......................................................................21
P4. Lags, Forward Leads, and Differences...........................................................................................22
P5. Generating Means and Other Statistics by Individual, Year, or Group..........................................22
Q. Panel Data Statistical Methods............................................................................................................22
Q1. Fixed Effects – Simplest Method...................................................................................................22
Q2. Other Panel Data Estimators..........................................................................................................23
Q3. Time-Series Plots for Multiple Individuals....................................................................................23
R. Probit and Logit Models.......................................................................................................................23
S. Other Models for Limited Dependent Variables..................................................................................24
S1. Censored and Truncated Regressions with Normally Distributed Errors......................................24
S2. Count Data Models.........................................................................................................................24
S3. Survival Models (a.k.a. Hazard Models, Duration Models, Failure Time Models).......................25
T. Instrumental Variables Regression.......................................................................................................25
T1. GMM Instrumental Variables Regression......................................................................................26
U. Time Series Models.............................................................................................................................27
U1. Autocorrelations.............................................................................................................................27
U2. Autoregressions (AR) and Autoregressive Distributed Lag (ADL) Models.................................27
U3. Information Criteria for Lag Length Selection..............................................................................27
U4. Augmented Dickey Fuller Tests for Unit Roots............................................................................28
U5. Forecasting.....................................................................................................................................28
U6. Newey-West Heteroskedastic-and-Autocorrelation-Consistent Standard Errors..........................28
U7. Dynamic Multipliers and Cumulative Dynamic Multipliers.........................................................29
V. System Estimation Commands............................................................................................................29
V1. Three-Stage Least Squares.............................................................................................................29
V2. Seemingly Unrelated Regression...................................................................................................30
V3. Multivariate Regression.................................................................................................................30
W. Other Estimation Methods..................................................................................................................30
W1. Nonlinear Least Squares...............................................................................................................30
X. Data Manipulation Tricks....................................................................................................................31
X1. Combining Datasets: Adding Rows...............................................................................................31
X2. Combining Datasets: Adding Columns..........................................................................................31
X2a. Unmatched Merge....................................................................................................................32
X2b. Matched One-to-One Merge....................................................................................................32
X2c. Matched Many-to-One Merge..................................................................................................33
X2d. Matched One-to-Many Merge.................................................................................................34
X2e. Matched Many-to-Many Merge...............................................................................................34
X3. Reshaping Data..............................................................................................................................34
X4. Converting Between Strings and Numbers....................................................................................35
X5. Labels.............................................................................................................................................35
X6. Notes..............................................................................................................................................36
X7. More Useful Commands................................................................................................................36
Useful Stata (Version 10) CommandsPreliminaries for RPI Dot.CIO LabsRPI computer labs with Stata include, as of Spring 2008: Sage 4510 and the VCC Lobby (all Windows PCs). Stata might also be available in Sage 3101 and Troy 2015 (try in the rooms to find out).To access the Stata program, look under My Computer and open the disk drive X: (which in our classroom is named “Sage4510$”), then double-click on the program icon that you see. You must start Stata this way – it does not work to double-click on a saved Stata file, because Windows in the labs has not been set up to know where to find Stata or even which saved files are Stata files.To access the course disk space, go to: \\\classes\econ-6570. If you are logged into the WIN domain you will go right to it. If you are logged in locally on your machine or into anther domain you will be prompted for credentials. Use:username: win\"rcsid"password: "rcspassword"substitiuting your RCS username for "rcsid" and your RCS password for "rcspassword". Once entered correctly the folder should open up.To access your personal RCS disk space from DotCIO computers, find the icon on the desktop labeled “Connect to RCS,” double-click on it, and enter your username and password. Your personal disk space will be attached as drive H. (Public RCS materials will be attached as drive P.) You will want to save Stata do-files to drive H. For handy use when logging in, you may also want to put the web address to attach the course disk space in a file on drive H; that way at the start of a session you can attach the RCS disk space and then open the file with your saved command and run it.A. Loading Dataset memory 100m Sets memory available for data to 100 megabytes. Clear before using. edit Opens the data editor, to type in or paste data. You must close the dataeditor before you can run any further commands.use "filename.dta" Reads in a Stata-format data fileinsheet using "filename.txt" Reads in text data.save "filename.dta" Saves the data.B. Variable Lists, If-Statements, and OptionsMost commands in Stata allow (1) a list of variables, (2) an if-statement, and (3) options.1. A list of variables consists of the names of the variables, separated with spaces. It goes immediatelyafter the command. If you leave the list blank, Stata assumes where possible that you mean all variables. You can use an asterisk as a wildcard (see Stata’s help for varlist). Examples:edit var1 var2 var3 Opens the data editor, just with variables var1, var2, and var3.edit Opens the data editor, with all variables.In later examples, varlist means a list of variables, and varname (or yvar etc.) means one variable. 2.An if-statement restricts the command to certain observations. You can also use an in-statement. If-and in-statements come after the list of variables. Examples:edit var1 if var2 > 3 Opens the data editor, just with variable var1, only for observations inwhich var2 is greater than 3.edit if var2 == var3 Opens the data editor, with all variables, only for observations in whichvar2 equals var3.edit var1 in 10 Opens the data editor, just with var1, just in the 10th observation.edit var1 in 101/200 Opens the data editor, just with var1, in observations 101-200.edit var1 if var2 > 3 in 101/200 Opens the data editor, just with var1, in the subset ofobservations 101-200 that meet the requirement var2 > 3.3.Options alter what the command does. There are many options, depending on the command – gethelp on the command to see a list of options. Options go after any variable list and if-statements, and must be preceded by a comma. Do not use an additional comma for additional options (the comma works like a toggle switch, so a second comma turns off the use of options!). Examples:use "filename.dta", clear Reads in a Stata-format data file, clearing all data previously in memory!(Without the clear option, Stata refuses to let you load new data ifyou haven’t saved the old data. Here the old data are forgotten andwill be gone forever unless you saved some version of them.) save "filename.dta", replace Saves the data, replacing a previously-existing file if any.You will see more examples of options below.C. Lowercase and Uppercase LettersCase matters: if you use an uppercase letter where a lowercase letter belongs, or vice versa, an error message will display.D. Review Window, and Abbreviating Command NamesThe Review window lists commands you typed previously. Click in the Review window to put a previous command in the Command window (then you can edit it as desired). Double-click to run a command. Also, many of the commands below can have their names abbreviated. For example, instead of typing “summarize”, “su” will do, and instead of “regress”, “reg” will do.E. Viewing and Summarizing DataHere, remember two points from above: (1) leave a varlist blank to mean all variables, and (2) you can use if-statements to restrict the observations used by each command.E1. Just LookingIf you want to look at the data but not change them, it is bad practice to use Stata’s data editor, as you could accidentally change the data! Instead, use the browser via the button at the top, or by using the following command. Or list the data in the main window, as noted below.browse varlist Opens the data viewer, to look at data without changing them. Close theviewer before using other commands.list varlist Lists data. If there’s more than 1 screenful, press space for the nextscreen, or q to quit listing.E2. Mean, Variance, Number of Non-missing Observations, Minimum, Maximum, Etc.summarize varlist Give summary information for the variables listed.summarize varlist, detail Give detailed summary information for the variables listed.E3. Tabulations, Histograms, Density Function Estimatestabulate varname Creates a table listing the number of observations having each differentvalue of the variable varname.tabulate var1var2Creates a two-way table listing the number of observations in each rowand column.tabulate var1var2, exact Creates the same two-way table, and carries out a statistical test of thenull hypothesis that var1 and var2 are independent. The test isexact, in that it does not rely on convergence to a distribution.tabulate var1var2, chi2 Same as above, except the statistical test relies on asymptoticconvergence to a normal distribution. If you have lots ofobservations, exact tests can take a long time and can run out ofavailable computer memory; if so, use this test instead.histogram varname Plots a histogram of the specified variable.histogram varname, bin(#) normal The bin(#) option specifies the number of bars. The normaloption overlays a normal probability distribution with the same meanand variance.kdensity varname, normal Creates a “kernel density plot”, which is an estimate of the pdf thatgenerated the data. The “normal” option lets you overlay a normalprobability distribution with the same mean and variance.E4. Scatter Plots and Other Plotsscatter yvar xvar Plots data, with yvar on the vertical axis and xvar on the horizontal axis.scatter yvar1 yvar2 …xvar Plots multiple variables on the vertical axis and xvar on thehorizontal axis.Stata has lots of other possibilities for graphs, with an inch-and-a-half-thick manual. For a quick web-based introduction to some of Stata’s graphics commands, try the “Graphics” section of this web page: /stat/stata/modules/. To tour Stata’s graphics capabilities from inside Stata, go Stata’s Help menu, choose “Stata Command…”, type “graph_intro”, and press return. Scroll down past the table of contents and read the section labeled “A quick tour.”E5. Correlations and CovariancesThe following commands compute the correlations and covariances between any list ofvariables. Note that if any of the variables listed have missing values in some rows, those rows are ignored in all the calculations.correlate var1var2 …Computes the sample correlations between variables.correlate var1var2 …, covariance Computes the sample covariances between variables.Sometimes you have missing values in some rows, but want to use all available data wherever possible – i.e., for some correlations but not others. For example, if you have data on health,nutrition, and income, and income data are missing for 90% of your observations, then you could compute the correlation of health with nutrition using all of the observations, while computing the correlations of health with income and of nutrition with income for just the 10% of observations that have income data. These are called “pairwise” correlations and can be obtained as follows: pwcorr var1var2 …Computes pairwise sample correlations between variables.F. Generating and Changing VariablesA variable in Stata is a whole column of data. You can generate a new column of data using a formula, and you can replace existing values with new ones. Each time you do this, the calculation is done separately for every observation in the sample, using the same formula each time.F1. Generating Variablesgenerate newvar = … Generate a new variable using the formula you enter in place of “…”.Examples follow.gen f = m * a Remember, Stata allows abbreviations: “gen” means “generate”.gen xsquared = x^2gen logincome = log(income) Use log() or ln() for a log-base-e, or log10() for log-base-10.gen q = exp(z) / (1 – exp(z))gen a = abs(cos(x)) This uses functions for absolute value, abs(), and cosine, cos(). Manymore functions are available – get help for “functions” for a list.F2. True-False Variablesgen young = age < 18 If age is less than 18, then young is “true”, represented in Stata as 1. Ifage is 18 or over, then young is “false”, represented in Stata as 0. gen old = age >= 18 If age is 18 or higher, this yields 1, otherwise this yields 0.gen age18 = age == 18 Use a single equal sign to set a variable equal to something. Use adouble equal sign to check whether the left hand side equals the righthand side. In this case, age18 is created and equals 1 if theobservation has age 18 and 0 if it does not.gen youngWoman = age < 18 & female==1 Here the ampersand, “&”, means a logical and. Thevariable youngWoman is created and equals 1 if and only if age isless than 18 and also female equals one; otherwise it equals 0.gen youngOrWoman = age<18 | female==1 Here the vertical bar, “|”, means a logical or. Thevariable youngOrWoman is created and equals 1 if age is less than18 or if female equals one; otherwise it equals 0.gen ageNot18 = age != 18 The “!=” symbol means “not equal to”.gen notOld = !old The “!” symbol is pronounced “not” and switches true to false or false totrue. The result is the same as the variable young above.When creating true-false values, note that missing values in Stata work like infinity. So if age is missing and you “gen old = age >= 18”, then old gets set to 1 when really you don’t know whether or not someone is old. Instead you should “gen old = age >= 18 if age<.”. This is discussed more in section K below.When using true-false values, note that 0 is false and anything else – including missing values – counts as true. So !(0) = 1, !(1) = 0, !(3) = 0, and !(.) = 0. Again, use an if-statement to ensure you generate non-missing values only where appropriate.F3. Random Numbersgen r = uniform() Random numbers, uniformly distributed between 0 and 1.gen n = 5 + 2 * invnorm(uniform()) Normally-distributed random numbers with mean 5 andstandard deviation 2. For other distributions use Stata’s menu to gethelp for “functions”.F4. Replacing Values of Variablesreplace young = age < 16 Changes the value of the variable young, to equal 1 if and only if age isless than 16.replace young = 0 if age>=16 & age<18 Changes the value of the variable young to 0, but only ifage is at least 16 and less than 18.F5. Getting Rid of Variablesdrop varlist Gets rid of all variables in the list.clear Gets rid of all variables, as well as other things not discussed yet (likeglobal variables, programs, etc.).F6. If-then-else Formulasgen realwage = cond(year==1992, wage*(188.9/140.3), wage) Creates a variable that uses oneformula for observations in which the year is 1992, or a differentformula if the year is not 1992. Stata’s cond(if, then, else) worksmuch like Excel’s IF(if, then, else). In this case, suppose you havedata from 1992 and 2004 only, and that the consumer price indexwas 140.3 in 1992 and 188.9 in 2004; then the example given herewould compute the real wage by rescaling 1992 wages while leaving2004 wages the same.F7. Quick Calculationsdisplay … Calculate the formula you type in, and display the result. Examplesfollow.display (52.3-10.0)/12.7display normal(1.96) Compute the probability to the left of 1.96 in the cumulative normaldistribution.display F(10,9000,2.32) Compute the probability that an F-distributed number, with 10 and 9000degrees of freedom, is less than or equal to 2.32. Also, there is afunction Ftail(n1,n2,f) = 1 – F(n1,n2,f). Similarly, you can usettail(n,t) for the probability that T>t, for a t-distributed randomvariable T with n degrees of freedom.F8. MoreFor functions available in equations in Stata, use Stata’s Help menu, choose Stata Command…, and enter “functions”. To generate variables separately for different groups of observations, see the commands in sections O and P5. For time-series and panel data, see section P, especially the notations for lags, leads, and differences in section P4. If you need to refer to a specificobservation number, use a reference like x[3], meaning the valuable of the variable x in the 3rd observation. In Stata “_n” means the current observation (when using generate or replace), so that for example x[_n-1] means the value of x in the preceding observation, and “_N” means thenumber of observations, so that x[_N] means the value of x in the last observation.G. Means: Hypothesis Tests and Confidence IntervalsG1. Confidence Intervalsci varname Confidence interval for the mean of varname (using asymptotic normaldistribution).ci varname, level(#) Confidence interval at #%. For example, use 99 for a 99% confidenceinterval.G2. Hypothesis Teststtest varname == # Test the hypothesis that the mean of a variable is equal to some number,which you type instead of the number sign #.。

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。