The purpose of this session is to introduce to STATA and gain some familiarity with a few STATA commands that will be used in future sessions.




Most STATA programs require only two lines, though there may be other lines added to transform data, calculate results, etc. These are:


use “”, clear
model commands


The first line reads in the data. The second line tells STATA which model to estimate and gives some options for the particular model.


You can enter STATA commands either from the STATA COMMAND window.  Or you can enter them into a batch file called a DO-FILE.  In this lesson we will use a DO-FILE, because it allows you to run blocks of commands in a batch mode and also save your commands into a file that can be used again.


As an example, enter STATA by clicking the STATA icon from the Windows desktop or program menu. Then click on the DO-FILE Editor Icon on the STATA menu. This opens a work area for editing a set of STATA commands. Now type in the following.


use "example.dta", clear

sum y x1 x2 x3

cor y x1 x2 x3

cor y x1 x2 x3, cov


Make sure that the path is correct for finding the file called “example.dta” on the first line.  Now, execute this program by clicking on the tools menu and select “do to bottom”.  This brief program reads in a data set contained in a file called “example.dta” and computes descriptive statistics on the variables in “example.dta.”


An alternative way to run this program would have been to mark the text and press the second icon from the left (do current file).


A fast way to mark all of the text in a command file is by pressing CTRL-A. To run an entire set of commands in a command window press CTRL-A and then execute in the above fashion.


Another way to run the entire program is to click on the last icon in the do-file (run current file).  This will run the entire do-file.  However, you will not see the output.


If you wish to run a subset of the do-file, highlight the lines you wish to run and select “do” in the tools menu or click on the second icon from the left (do current file). This is often useful for de-bugging programs.


Now let's consider what was in the command file we just executed.

The "use" option tells STATA to use the data set that follows, “example.dta.”  The command “clear” tells STATA that it may delete any data set that exists in memory. 


 The “sum” command is short for “summarize” and tells STATA to do descriptive statistics on variables y,x1,x2,x3. The command “cor” tells STATA to report the correlations that exist between the variables.  Adding the command “,cov” tells STATA to report the covariances.


Note that the typical STATA command begins with a keyword  (e.g., use, sum, reg, gen,…) that alerts the program that a command has begun.   Keywords are usually the first three letters of a model or other command.  It is ok to use the entire word, but it is more parsimonious to abbreviate.  One important point is that STATA is case sensitive in regards to variables and commands.  All commands in STATA are lower-case.


You may save a set of commands from the command file by selecting FILE SAVE or SAVE-AS from the STATA do-file menu. You will be prompted for a file name for the saved file that can then be used for later work.  If you wish to save changes to the data, go to the main STATA menu and select FILE SAVE or SAVE-AS.

You may also want to print results or save them for viewing. To print, select FILE PRINT RESULTS from the main STATA menu.  To save output from a STATA session you may type “log using “filename”, replace” in the beginning of the do file.  NOTE: This will overwrite any previously created file by the same name. 


MODEL Commands


STATA has many "canned" statistical procedures that can be executed using a single MODEL statement with options. Many other statistical procedures can be performed by creating a program for function optimization using the maximum likelihood features of STATA. We will use this latter approach frequently in this course. Below is a listing of models that can be called in the MODEL line. Most of the procedures have numerous options, so this is merely an overview. The quick reference guide distributed with the STATA manual lists the full set of options. For a full description of each model go to the STATA HELP menu, enter the model command, and choose from the listed options. You should pay particular attention for now to the “summarize”, “regress”, and “program” commands that will be used over the next few sessions. Here is a list of "canned" STATA procedures.


arima Box-Jenkins ARIMA models.
biprobit Bivariate probit models.
boxcox MLE or nonlinear least squares for Box-Cox model.
tabulate Cross-tabulation. Frequency counts and contingency tables.
nlogit Random utility models and nested logit models.
sum Descriptive statistics.
graph there are a number of graphing options availible in STATA.
truncreg Completely censored data.
hist Histogram
robust following a regression equation transforms estimates the Huber/White Sandwich estimator of variance.
corrgran Plot autocorrelations and partial autocorrelations.
logit Binomial or multinomial logit model.
lnormal estimates maximum-likelihood log-normal distribution (survival time) models

biprobit estimates maximum-likelihood two-equation probit models -- either a bivariate probit or a seemingly unrelated probit (limited to two equations).  For partial observability or sample


nbreg Negative binomial regression models.
nlogit Nested logit and conditional logit models.
suest seemingly unrelated regressions (web install).
oprobit Ordered probit or logit models.
probit Univariate probit model.
tobit Censored regression.
truncreg Truncated regression
dotplot Scatter diagrams.
poisson  Poisson and negative binomial regression models.
regress (or reg) Classical regression. Least squares regression.
streg Analysis of duration data.
switchr switching regressions (web install)
xtreg Time series/cross section regressions.
testnl Test restrictions or obtain variances for nonlinear functions.
ivreg Two stage least squares.
reg3 Three stage least squares.


Transformations and Calculations


The basic command for creating new variables is “generate”. The format for the “generate” command is:

generate newvar=function


wherenewvar” is the new variable name and function is a mathematical function. For a list of available functions see the STATA help guide.


If you want to see a listing of the new variable, use:


list newvar


An extended set of variable transformations is available through STATA’s “egen” command. The format is the same as above. See “help egen” for more information.


The basic command for working with scalars is “scalar”. The format for this command is:

scalar newscalar=number


wherenewscalar” is the new scalar created. If you want to see a listing of the scalar use:


display newscalar


ASSIGNMENT: As an exercise for this first session, read the data from the data file called “example.*.” Use the “generate” command to create some new variables, x1, x2, x1*x2 and x3 squared (See STATA help file). Compute descriptive statistics on these variables. Use the “regress” command to do a regression of y on a constant, x1*x2, and x3 squared. (See STATA help file for regression). In your regression list and save the residuals and predicted values to new variables, plot the residuals, and obtain CUSUM plots for parameter stability. Also, use the “fitstat” option to save and view the log likelihood.


Try this first independently. However, if you get into trouble, here is what the command file would look like (“fitstat” and “cusum6” must be downloaded and installed through STATA help).


/* How to get data into  STATA */


/* From ascii text with no variable names */

infile Y X1 X2 X3 using "C:\Users\wood\Documents\My Teaching\Maximum Likelihood\Data\EXAMPLE NONames.txt", clear


/* From an Excel spreadsheet converted to .txt or .csv */

insheet using "C:\Users\wood\Documents\My Teaching\Maximum Likelihood\data\example.txt", clear


insheet using "C:\Users\wood\Documents\My Teaching\Maximum Likelihood\data\example.csv", clear


/* From an SPSS .sav file you need the user written procedure "usespss" */

/* Package must be installed to use it. */

usespss using "C:\Users\wood\Documents\My Teaching\Maximum Likelihood\data\example.sav", clear


/* To convert any of these to a STATA data file, just select File, Save As from the

STATA menu. For more information on reading and creating data files, go to the STATA HELP menu, select data, and read chapter 24.



/* From a STATA data file */

use "C:\Users\wood\Documents\My Teaching\Maximum Likelihood\data\example.dta", clear


/* Save your output to a file. */

log using “examplelog”, replace


/* Compute Various Summary Statistic */

sum y x1 x2 x3

cor y x1 x2 x3

cor y x1 x2 x3, cov


/*Transform Variables */

generate x1x2=x1*x2

generate x3sqr=x3^2

sum x1x2 x3sqr


/* Run a Linear Regression and Get Some Output */

regress y x1x2 x3sqr

predict yhat

predict e, resid

fitstat, saving (logl)

list yhat e


/* Graph the Residuals against Y */

graph twoway (scatter y e)


/* Test Coefficient Stability */

tsset time

cusum6 y x1x2 x3sqr


/* Close the Log File */

log close