Introduction to STATA
Contents
1. edit varlist Variable and data types STATA supports six variable types which are grouped into real numbers float double integer byte int long and string str The default type is float single precision real number STATA stores or formats data in either of two ways numeric or string Numeric will store numbers while string will store text Stings can also be used to store numbers but you will not Econometrics 2 M Scholz Introduction to STATA Summer 2008 be able to perform numerical analysis on those numbers Note with string variables you must enclose the observation reference in double quotes Otherwise STATA will claim not to be able to find what you are referring to For example Summarize if make Dodge Colt When writing commands missing numeric observations are denoted by a single dot and miss ing string observations are denoted by blank quotes Examining the data It is very important to examine your data when you first read it into STATA you should check that all the variables and observations are read in as expected e summarize This provides summary statistics such as means standard deviations and so on Summarize Figure 3 Summarize This information is useful to double check for problems with the data e g if the min of some variable is negative this might indicate an error in the way the numbers were entered into STATA As an aside most STATA comm2. degrees of freedom is 101 25 The p value or significance level of the test is zero so we can reject the null hypothesis even at the 1 level weight is significantly different from zero Notice that the F distribution with 1 numerator degree of freedom is identical to the distribution so the F test result is the same as the square of the t test result in the regression Also the p values associated with each test agree If you want to you can test any linear hypothesis about th coefficients such as reg mpg weight weightsq foreign test 3 weight 0 5 weightsq 2 foreign If you want to jointly test a number of restrictions use the accumulate option test weight 0 test foreign 3 accum or you can simply use test weight 0 foreign 3 test weight Figure 8 F test 12
3. the first command and execute it hit Return or Enter then the next and so on e do file type up a list of commands in a do file essentially a computer program and exe cute the do file Directories and folders Like Dos and Windows STATA can organise files in a tree style directory with different folders You can use this to organise your work in order to make it easier to find things at a later date For M Scholz Summer 2008 Econometrics 2 Introduction to STATA print data editor Figure 2 The STATA Toolbar open open a STATA dataset save save a dataset print print contents of active window log to start or stop pause or resume a log file viewer open viewer window or bring to the front results open results window or bring to the front graph open graph window or bring to the front do file editor open do file editor or bring to the front data editor data browser more break open data editor or bring to the front open data browser or bring to the front command to continue when paused in long output stop the current task Table 1 The STATA Toolbar example create a folder data to hold all the datasets you use sub folders and so on You can use some Dos commands in STATA including cd mkdir dir Note STATA is case sensitive so it will not recognise the command CD or Cd Also quotes are only needed if the directory or folder names has spaces in it c temp Stata fold
4. those with more than 3 repairs in 1978 keep if rep78 gt 3 drop if rep78 lt 3 Note the different relational operators are Econometrics 2 M Scholz Introduction to STATA Summer 2008 equal to not equal to gt greater than gt greater than or equal lt less than lt less that or equal Keeping observations only for the cars with repair records between 2 and 4 keep if rep78 gt 2 amp rep78 lt 4 drop if rep78 lt 2 rep78 gt 4 Or keep that cars which have zero or 1 and 4 or 5 repairs in 1978 keep if rep78 gt 0 amp rep78 lt 2 rep78 gt 3 amp rep78 lt 5 Note the different logical operators are amp and or or not You may want to drop observations with specific values such as missing values denoted in STATA by a dot drop if rep78 e sort For many STATA operations it is important what order your observations are in Thus sometimes you have to sort the data sort price This puts the observations in ascending order browse to check e by processing You can re run a command for different subsets of data using the by prefix For example to get summary statistics of price broken down by rep78 sort rep78 by rep78 sum price Note for this operation you have to sort the data first The by prefix causes the sum com mand to be repeated for each unique value of the variable rep78 The result is the same as writing a list of sum commands
5. 78 gen rdum This creates a dummy variable rdum1 equal to one if the rep78 variable is 1 or whatever is the first entry in alphabetic order and zero otherwise a dummy variable rdum2 if the entry is 2 and so on up to rdum5 You can refer to this set of dummies in later commands using a wild card e g rdum instead of typing out the entire list e Lags and leads Suppose that the variable date contains quarterly date information say 19471 19472 19473 19474 and then jumps to 19481 To generate a lagged price variable we need our date variable to run continuously in regular increments One way to solve this would be to use the actual observation number denoted _n as they run continuously from 1 2 3 right up to 74 in single units First make sure to sort the data so that the dates so run in chronological order so date gen period _n gen lagprice price _n 1 if period period _n 1 1 This lags every observation by one quarter The if argument avoids problems when there are gaps in the dates You need to make sure that you are lagging the values of the previous period s data only if the dataset only had observations for 19471 and 19501 20014 then lags will only be created for 19502 on A lead can be created in similar fashion gen leadprice price _n 1 if period period _n 1 1 Do files and log files Instead of typing commands one by one interactively you can type them all in one go within a do file and
6. Econometrics 2 M Scholz Introduction to STATA Summer 2008 Introduction to STATA What STATA looks like The STATA package is located under Start gt Programs gt STATA You can also run the program by double clicking a STATA data set dta STATA is a command driven package You type Past commands appear here Results appear here HE stata SE 8 0 is 5 x File Edit Prefs ata Graphics Statistics User Window Help sjaj S amle s olal olo E Stata Results TEKE cd e stata a log using intro log replacr su sort remsemo caseid pers d dr do e lfs remsemo do d if remserno remsemoL save replace regress grsswk age sex e d Y Target Command Window resbby schm39 ndtype newdeal teclec ytetmp ytetib wrking jbaway log on text ownbus telbus everwk leftyr e stata Working directory displayed here Displays destination of variables clicked in window below Variable list Commands typed displayed here appear here Figure 1 Screenshot Log status appears here in the commands you want rather than pick them from pull down Windows menus It has many in build commands that can make easy work of complicated statistical or econometric routines Furthermore you have the possibility to create your own commands to do anything that STATA is not equipped to do itself You can enter commands in either of two ways e interactively type
7. ack over it you know what you were trying to achieve with each command or set of commands You can insert notes in two different ways e STATA will ignore a line if it starts with an asterisk so you can type whatever you like on that line Note the asterisk is also useful for getting STATA to temporarily ignore commands if you decide later to re insert the command into your do file just delete the asterisk e You can place notes after a command by inserting it inside these pseudo parentheses for example use h auto dta clear opens the auto data These pseudo parentheses are also useful for temporarily blocking a whole set of commands 10 Econometrics 2 M Scholz Introduction to STATA Summer 2008 place at the beginning of the first command at the end of the last and STATA will just skip over them all Estimation e Linear estimation A straightforward OLS regression of mileage on weight weight and foreign gives the results presented in Figure 6 reg mpg weight weightsq foreign reg mpg weight weightsq foreign Figure 6 Regression There are a few points to note here The first variable listed after the regress or reg for short command is the dependent variable and all subsequently listed variables are the independent variables STATA automatically adds the constant term or intercept to the list of independent vari ables type reg ce gdp noconstant if you want
8. ands can be abbreviated which saves some typing Thus for example type sum instead of summarize The abbreviations are noted in the STATA manuals e graph You can graph a simple histogram with the command graph twoway histogram rep78 This automatically calculates the dimensions of the histogram e g number and width of bins bars You also can override the defaults To draw a two way scatterplot graph twoway scatter price mpg Econometrics 2 M Scholz Introduction to STATA Summer 2008 o M 3 Repair Record 1978 Figure 4 Histogram Saving the dataset The command is save or sa for short sa c auto dta replace The replace option overwrites any previous version of the file in the directory you try to saving to If you want to keep an old version as back up you should save under a name such as auto_new Preserve and restore If you are going to make some revisions to the dataset but are unsure of whether or not you will keep them then you have two options First you can save the current version make the revisions and if you decide not to keep them just re open the saved version Second you can use the preserve and restore commands preserve will take a photocopy of the dataset as it stands and if you want to revert back to that copy later on just type restore Organising datasets e rename You may want to change the names of your variables perhaps to make it more transpa
9. er but it s a good habit to use them all time Getting help e Manuals The User Manual provides an overall view on using STATA There are also a num ber of Reference Volumes which are basically encyclopedias of all the different commands and all you ever needed to know about each one If you want to find information on a par ticular command or a particular econometric technique you should first look up the index at the back of any manual to find which volumes have the relevant information Finally there is a separate Graphics Manual e Stata s in built help and website STATA also has an abbreviated version of its manuals built in Click on Help then Contents STATA s website has a very useful FAQ section at Econometrics 2 M Scholz Introduction to STATA Summer 2008 http www stata com support faqs Both the in built help and the FAQs can be simulta neously searched from within STATA itself Help then Contents Helpful links are listed at http www stata com links resources1 html Many researchers provide their own STATA programs on STATA s webpage net search keyword searches the internet for user written additions to STATA that contain the specified keyword Reading data into STATA There are different ways of reading or entering data into STATA e use If your data is in STATA format then simply read it in as follows use c auto dta e insheet If your data is originally in Excel o
10. g numeric observations denoted by a dot are interpreted by STATA as a very large positive number You need to pay special attention to such observations when using if statements If the last command listed above had simply been gen small mpg if price lt 4000 then small would have included any observation with missings The egen command typically creates new variables based on summary measures such as sum mean min and max egen totprice sum price by foreign egen avgprice mean price by foreign egen maxprice max price Note that both gen and egen have sum options egen generates the total sum and gen creates a cumulative sum The running cumulation of gen depends on the order in which the data is sorted so use with caution The replace command modifies existing variables in exactly the same way as gen creates new variables gen lprice 1n price replace lprice 0 if lprice missing now 0 e Dummy variables You can use gen and replace to create dummy variables as follows gen smallprice 0 replace smallprice 1 if price lt 4000 amp price Or you can combine these in one command Econometrics 2 M Scholz Introduction to STATA Summer 2008 gen smallprice price lt 4000 amp price Note the parenthesis are not strictly necessary but can be useful for clarity purposes If you want to create a set of dummy variables for example one for each rep78 use tab rep
11. ou might have accidentally left open If there were no log file actually open then the command log close on its own would stop the do file running and give the error message no log file open Using capture tells STATA to ignore any error messages and keep going log using projecti log replace starts a log file of all the results The replace option overwrites any og file of the same name If instead you want to add the new log file to the end of previous versions then use the append option set more off when there are a lot of results in the results window STATA pauses the do file to give you a chance to review each page on screen and you have to press a key to get more This command tells STATA to run the entire do file without pausing You can then review the results in the log file set memory 10m STATA s default memory may not be big enough to handle large data files Trying to open a file that is too large returns a long error message beginning no room to add more observations You can adjust the memory size to suit Note however that setting it too large can take the PC s memory away from other applications and slow the computer down so only set it as large as necessary log close closes the log file Remember to include a lt Return gt or lt Enter gt at the end of the last command to ensure that STATA executes it Comments It is good practice to keep extensive notes within your do file so that when you look b
12. r some other format you need to prepare the data before reading it directly into STATA You need to save the data in the other package e g Excel as either a csv Comma separated values or txt ASCII text file There are some rules to be followed when saving a csv or txt file for reading into STATA The first line in the spreadsheet should have the variables names e g make price mpg and the second line onwards should have the data If the top row contains a title delete this row Any extra line below the data or to the right of the data will also be read in by STATA so make sure that only the data itself is in the spreadsheet If necessary select all the bottom rows and or right hand columns and delete them Some notation for missing values can confuse STATA e g it will read double dots or hyphens as text Use find amp replace to get symbols with single dots or simply to delete them altogether Once the csv or txt file is saved you then read it into STATA using the command insheet using c auto csv e The data in the active datafile can be browsed read only in the Browser window which is activated from the menu Data Data browser by clicking the browse icon or by browse varlist where the varlist is a list of variables to be displayed The Editor window allows to edit data either by directly typing into the editor windor or by copying and pasting from spreadsheets software
13. rent what the variable is ren mpg mileage ren rep 78 repair_record Note you can only rename one variable at a time e recode You can change the values that certain variables take e g suppose that the trunk space is 13 instead of 11 in all possible cases Econometrics 2 M Scholz Introduction to STATA Summer 2008 e S 9 e e e J e e Q S e o e J ay A 2 e e s ad ae nn e SF e e e 5 Poje tu S e J aad 10 20 30 40 Mileage mpg Figure 5 Scatterplot recode trunk 11 13 Note recode only allows numeric variables This command can also be used to recode missing values to the dot that STATA uses to denote missings Suppose a dataset codes missing rep78 values as 999 recode rep78 999 keep and drop including some notes on if processing The original dataset may contain variables you are not interested in or observations you don t want to analyse It s a good idea to get rid of these first so they won t use valuable memory and these data won t inadvertently sneak into your analysis You can tell STATA to either keep what you want or drop what you don t want the end results will be the same keep make price mpg rep78 weight foreign drop headroom trunk length turn displacement gear_ratio Each of these will leave you with the same set of variables you can double check this in the variables window You can also drop or keep observations such as
14. simply run the do file The result of each command can be recorded in a Jog file for review when th do file is finished running The vast majority of your work should use do files If you have a long list of commands executing a do file once is a lot quicker than executing several commands one after another Furthermore the do file is a permanent record of all your commands and the order in which you ran them This is useful if you need to tweak things or correct mistakes instead of inputting all the commands again one after another just modify the do file and re run it Do files can be written in any text editor STATA also has its own editor built in click the icon along the top of the screen with the pad and pencil logo Most do files follows the following format version 8 0 clear cd h projects project1 capture log close log using projecti log replace Econometrics 2 M Scholz Introduction to STATA Summer 2008 set more off set memory 10m LIST OF COMMANDS log close To explain the different commands version STATA is backward compatible so programs created using version 8 can still be run when version 9 comes along by just entering this command at the start clear clears any data currently in STATA s memory cd h projects project1 sets the default directory where STATA will look for any files you try to open and save any files you try to save capture log close closes any log files that y
15. to exclude it The top left corner gives the ANOVA decomposition of the sum of squares in the de pendent variable Total into the explained Model and unexplained Residual The top right corner gives the statistical significance results for the model as a whole e g R squared The bottom section gives the results for the individual independent variables e g stan dard errors You can run regressions on a sub sample using if processing e g reg mpg weight weightsq foreign if price lt 4000 e Instrumental variable regression Theoretically we could instrument the independent variable weight with other observed variables cf Figure 7 ivreg mpg foreign weight headroom trunk length Hypothesis testing The results of each estimation automatically include for each independent variable a t test for linear regressions and a z test for regressions such as logit or probit on the null hypothesis that the true coefficient is equal to zero You can also perform an F test or 77 test on this hypothesis using the test command 11 Econometrics 2 M Scholz Introduction to STATA Summer 2008 ivreg mpg foreign weight headroom trunk length Figure 7 IV regression ivreg mpg foreign weight headroom trunk length test weight 0 or since STATA defaults to comparing the listed terms to zero you can simply use test weight The F statistic cf Figure 8 with 1 numerator and 71 denominator
16. with separate if statements for each possible repair record sum price if rep 78 sum price if rep 78 2 Sum price if rep 78 3 Sum price if rep 78 4 Sum price if rep 78 5 This example may not be very useful but if you had a dataset for different countries then you may want to run some commands for the different country subsets Econometrics 2 M Scholz Introduction to STATA Summer 2008 Creating new variables oe 99 e System variables The following system variables note the _n contains the number of the current observation _N contains the total number of observations in the dataset _pi contains the value of m to machine precision may be useful e Generate egen replace The two most common commands for creating new variables are gen and egen You can create a host of new variables from the existing data with the gen command not all of the following examples have a senseful meaning gen range mpg trunk 1 609344 division multiplication gen vari headroom length 100 addition subtraction gen dif abs displacement trunk absolute difference gen lprice ln price natural logarithm gen weightsq weight 2 square gen cumprice sum price running cumulative gen ten 10 constant value of 10 gen id _n id number of observation gen small mpg if price lt 4000 amp price mpg for small prices Note missin
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