Washington & Lee’s
Guide to Stata
Niels-Hugo Blunch
and
Carol Hansen Karsch
Table of Contents
Stata Resources...........................................................................................................................................................1
Components of Stata: .................................................................................................................................................1
Stata Basics: ...............................................................................................................................................................2
Opening a Stata File ...........................................................................................................................................2
. use .............................................................................................................................................................2
. set memory................................................................................................................................................2
Command Syntax ...............................................................................................................................................2
Making Stata Stop ..............................................................................................................................................3
Restricting the Data ............................................................................................................................................3
“Help” in Stata ....................................................................................................................................................3
Interactive vs. Batch Mode .........................................................................................................................................4
Interactive Mode .................................................................................................................................................4
Batch Mode Using Do Files ...............................................................................................................................4
Best of Both Approach .......................................................................................................................................4
Documenting A Stata Session – The Log - File: ...............................................................................................4
Importing Data into Stata: ..........................................................................................................................................5
Examining the Data ....................................................................................................................................................6
. describe .............................................................................................................................................................6
. summarize varname, detail ...............................................................................................................................6
. codebook varname ............................................................................................................................................6
. tabulate varname ...............................................................................................................................................7
. tabulate varone vartwo......................................................................................................................................8
. tabstat varone vartwo, etc. ................................................................................................................................8
. histogram varname ...........................................................................................................................................9
. graph box varname ...........................................................................................................................................9
. tsline varname .................................................................................................................................................11
Verifying the Data ....................................................................................................................................................12
. assert ...............................................................................................................................................................12
Numeric Missing Values ..................................................................................................................................12
. correlate - Checking For Correlation ..............................................................................................................13
Creating & Modifying Variables ..............................................................................................................................14
CREATING NEW VARIABLES....................................................................................................................14
Variable Names: .......................................................................................................................................14
i
= Versus == ..............................................................................................................................................14
. generate...........................................................................................................................................................14
Addition/Subraction ..................................................................................................................................14
Division /Multiplication (Interaction) .......................................................................................................14
Exponentiation ..........................................................................................................................................14
Lag ............................................................................................................................................................15
Log: ...........................................................................................................................................................15
. egen.................................................................................................................................................................15
Dummy Variables .............................................................................................................................................15
The Easy Way ...........................................................................................................................................15
Second Method .........................................................................................................................................16
Third Method ............................................................................................................................................16
Dates .................................................................................................................................................................17
MODIFYING VARIABLES ............................................................................................................................17
String Variables ........................................................................................................................................17
. destring ...................................................................................................................................................17
. replace .....................................................................................................................................................18
. recode ......................................................................................................................................................18
Modifying and Combining Files ..............................................................................................................................19
Modifying a File ...............................................................................................................................................19
. keep & . drop ..........................................................................................................................................19
. move & . order : reordering the list of variables .....................................................................................19
. sort ..........................................................................................................................................................19
. reshape (wide  long or long  wide) ..................................................................................................20
Combining Files ...............................................................................................................................................21
. append .....................................................................................................................................................21
. merge ......................................................................................................................................................21
Time Series and Panel Data ..............................................................................................................................22
Time Series Operators ..............................................................................................................................23
Regression Analysis .................................................................................................................................................24
OLS (Studenmund, pp. 35 – 40) .......................................................................................................................24
Restricting Stata commands to observations with certain conditions satisfied.........................................24
Ensuring Consistency Among the Number of Observations in the various estimation samples ..............24
Ramsey’s Regression Specification Error Test (RESET) (Studenmund, pp. 198 - 201) ..........................25
ii
Akaike’s Information Criterion and the Schwartz (Bayesian Information) Criterion (Studenmund, p.
201) ...........................................................................................................................................................25
Detection of Multicollinearity: Variance Inflation Factors (Studenmund, p. 259) ...................................25
Serial Correlation ..............................................................................................................................................25
Durbin-Watson D Test for First-Order Autocorrelation (Studenmund, p. 325) ........................................25
Dealing With Autocorrelation ..................................................................................................................26
GLS Using Cochrane-Orcutt Method (Studenmund, p. 332) ...................................................................26
Newey-West standard errors (Studenmund, p. 334) .................................................................................26
Heteroskedasticity ............................................................................................................................................26
White test (Studenmund, p. 360) .............................................................................................................26
Weighted-Least Squares (WLS) (Studenmund, p. 363) ...........................................................................26
Heteroskedasticity-corrected (HC) standard errors (Studenmund, p. 365) ...............................................26
Hypothesis Testing ...................................................................................................................................................27
t-tests.................................................................................................................................................................27
f-tests ................................................................................................................................................................27
Tests for joint statistical significance of explanatory variables ................................................................27
Testing for equality of coefficients ...........................................................................................................27
Making “Nice” Tables of Regression Results ..........................................................................................................28
. outreg2 ............................................................................................................................................................28
The “estimates store” and “estimates table” Commands: .................................................................................28
More Advanced Regression Models ........................................................................................................................30
. logit & . probit models ...................................................................................................................................30
Probit with marginal effects reported .......................................................................................................30
Logit with marginal effects reported.........................................................................................................30
Multinomial logit ......................................................................................................................................30
Appendix A ..............................................................................................................................................................32
Do File Example ...............................................................................................................................................32
Appendix B ..............................................................................................................................................................33
Importing an Excel file into Stata .....................................................................................................................33
Appendix C ..............................................................................................................................................................34
Downloading Data from ICPSR .......................................................................................................................34
Using a Stata Setup File to Import ASCII Data into Stata................................................................................35
iii
STATA RESOURCES
Stata’s own website is the natural starting place – the following link leads you to a wealth of info on Stata,
including Tutorials, FAQs, and textbooks:
http://www.stata.com/links/resources.html
One of the most helpful links found on the Stata site is UCLA’s Stata Resources website. It includes a set of
learning modules:
http://www.ats.ucla.edu/stat/stata/modules/default.htm
COMPONENTS OF STATA:
Stata opens several windows when it launches. A window can be opened and made active either by clicking on it
with the mouse or by selecting it from the “Window” menu. Right-clicking on a window displays options for that
window.
1) Results are displayed in the Results window.
2) Commands are entered into the command line from the Command window.
3) Previously entered commands are displayed in the Review window.
4) The variables for the current dataset are displayed in the Variables window.
a) The Variables window can be used to enter variable names into the Command window (simply
click on a variable name)
b) In version 10 of Stata, right clicking on a variable in the window gives the options to rename,
create or edit a label, or change a variable’s format.
The Stata toolbar provides access to commonly used features:
1) Holding the mouse pointer over a button displays a description of the button.
2) A window can be opened by clicking its button on the toolbar.
Menus and dialogs can provide access to commands in Stata until the time comes when you want to directly enter
the command in the command window.
The working directory is displayed on the status bar in the lower left-hand corner.
1
STATA BASICS:
Stata is case sensitive. Commands are written in lower-case. Variable and file names can be in upper and/or lower
case, but consistency is necessary. Stata interprets GDP and gdp as two different variables that can co-exist in the
same file.
OPENING A STATA FILE
. use
The easiest way to open a Stata file is to double click on it in the directory. If you are already in the program, the
the menu system is the next easiest: click on the File menu, select Open and then browse to your file.
Alternatively, type the command: . use filename in the command window or in a batch file. A file cannot be
opened when another file is already open. Either close the first file or use the replace option in the . use
command.
. use filename, replace
. set memory
Stata tries to load an entire file into memory. A problem will arise if you try to open a large file and insufficient
memory has been allocated to the program. You’ll get an error message that states that there is “no room to add
observations.” To assign more memory to Stata, use the . set memory command. For computers on campus, you
could easily set memory to 300m or 300000k, maybe more, if necessary. Then use the menu system or the . use
command to open the file from within that copy of the Stata program. Do NOT double click on the file in the
directory. That launches another copy of the Stata program with the default amount of memory allocated to it, and
you’ll get the error message again. It is just like having two copies of MS Word or Excel open, one with the
default memory and one with the increased amount of memory.
COMMAND SYNTAX
Stata commands have a common syntax, which is written in the following manner:
. prefix_cmd: command varlist if exp in range, options
In a lot of Stata documentation, commands are preceded by a “.” and that convention will be followed in this
guide. Do NOT type the period!!!!
Commands can be extremely simple:
. list
will cause Stata to list the entire file. No need to use a menu to issue that command!
Obviously, commands can be more complex and powerful. Following the standard syntax, the list command
illustrated below, (abbreviated to just . l), instructs Stata to list only the mpg, price and weight variables, if the
price is greater than 20,000 in observations 1 through 100 by groups based on the values of the variable rep78.
The option “clean” instructs the program to list the observations without boxes around each variables which
makes the listing nice and compact.
. by rep78: l mpg price weight if price > 20000 in 1/100, clean
By default, observations are listed a screen full at a time. To get to the next screen of results, click on the blue
– more – command that appears at the end of each screen.
To change the default setting to get an uninterrupted flow of results, type in the command:
2
. set more off
To switch back to the default of one screen full of output at a time, use the command:
. set more on.
MAKING STATA STOP
To stop the output, whether streaming or paused at a - more - prompt, click on the break button, the red circle with
the white “X”, located in the toolbar under the Help menu.
RESTRICTING THE DATA
The if and in qualifiers are extremely useful and work with many commands. They are used to restrict the
operation of a command to observations that meet the specified conditions. In the example above, the . list
command was restricted to observations with vehicles whose prices exceeded $20,000 within the first 100
observations.
The results were also restricted to include only the variables contained in the varlist (mpg, price and weight.)
Other variables in the dataset would not appear in the resulting list.
For more examples of using the if qualifier, especially in regression models, see: “Restricting Stata commands to
observations with certain conditions satisfied” in the “Regression Analysis” section.
A typical example of a prefix_cmd is by which can be used to divide the data up into groups.
“HELP” IN STATA
Stata’s “Help” utility is very good. Use it to see how to use commands. Issuing the command ". help
<XXXXXX>", for example, ". help summarize", will make Stata search through all the official Stata files.
Descriptions will use the common Stata syntax described above.
Alternatively issuing the command ". findit <XXXXXX>", will make Stata search the Web in addition to the
official Stata files. This is particularly useful if searching for a keyword, concept and/or a Stata command that is
NOT part of official Stata (there are many user-written Stata commands on the Web that can be downloaded into
official Stata this way, by following the appropriate links resulting from the search.)
3
INTERACTIVE VS. BATCH MODE
INTERACTIVE MODE
In interactive mode, commands are typed “as you go” into the command window. The drawback is that when you
end your Stata session, all your work is gone… (EXCEPT if you created a “log-file,” see below.)
BATCH MODE USING DO FILES
In batch mode, commands are written and saved as do files. Type the Stata commands directly into the “New Dofile Editor” (the fifth-from-last icon on the toolbar,) one command per line, the same way you would have done in
interactive mode. Then SAVE the file before running it by clicking the “Do current do-file” button in the Do-file
Editor.
Advantages of using do files include:
1) Reproduce results exactly – and fast!!
2) Continue analyses from the point where you left off.
3) Recall your reasoning. Comments in do-files serve as memory aids, which can be useful six months
later or as documentation for others trying to understand your work. Enclose multiple lines of
comments by beginning the line with a "/*" and closing with a "*/". A single comment line can begin
with a “*” or a “//”. The “//” can also be used to include comments on the same line as a command.
Examples of all three types of comment codes are illustrated in the sample do file in Appendix A.
BEST OF BOTH APPROACH
Alternatively, combine the interactive and batch modes. Figure out the commands while working in interactive
mode, then copy the error-free commands from the Results or Command windows and paste them into the Do-file
Editor. This approach takes discipline. It can be hard to make yourself stop and do this bit of housekeeping, but it
is usually time extremely well spent. So, it is highly recommended.
DOCUMENTING A STATA SESSION – THE LOG - FILE:
As explained above, do-files; except for initial, exploratory work; are the best way to document your work
because results can always be replicated, and they serve as documentation of your Stata session. There is a
possibility of documenting your entire Stata session, however, no matter whether you work in interactive or batch
mode – this is the so called “log-file,” which is a file containing everything you did in your Stata-session.
To start a log file interactively, simply click on the “Begin Log” icon. It is bronze-colored and is between the print
and viewer (eye) icons on the toolbar.
To start a log file in a do file, use the command:
. log using "C:\XXXX\YYYYY\ZZZZZ.log", replace
where “X”, “Y” and “Z” specify the relevant path. The “replace” option gives permission to the system to
overwrite a previous log-file of the same name. If no pre-existing log-file is found, Stata will send an innocuous
message stating that fact. As an alternative, the “append” option instructs the program to add the current log
session to the end of an existing log-file of the same name.
4
IMPORTING DATA INTO STATA:
There are several ways to import data into Stata (listed in order of ease on your part):
1) Load a pre-existing Stata file (*.dta)
2) Copy & paste from a spreadsheet, such as Excel, into Stata’s Data Editor (feasible only for a
relatively small dataset.)
3) Type the numbers into Stata’s Data Editor (ditto #2.)
4) Use the “. insheet”, “. infile”, or “. infix”, command. These are relevant for Excel files, freeformatted ASCII (text) files, etc. See Appendix B for step by step instructions for using the . insheet
command to import an Excel file into Stata. For additional information, type the command . help
infiling in the command window to get an overview of all three methods.
5) Use a setup file and a data dictionary. This is typically the situation when using data from ICPSR. See
Appendix C for more information.
Unless you began with a Stata data file, once the data is loaded, save it as a Stata file, so that it will be available
for future analyses using the command:
. save " C:\XXXX\YYYYY\ZZZZZ.dta", replace
This command was probably already included in the setup file in method #5.
If methods #2 or #3 were used, rename the variables from the default names of "var1", "var2", etc., to more
descriptive names. See . rename in “Creating & Modifying Variables” in the next section. You can of course
rename variables created via any of the other methods, but presumably those names should already be relatively
descriptive.
5
EXAMINING THE DATA
The following are a few commands that will familiarize you with a data file. The next several examples use the
auto.dta dataset that comes installed with Stata. Type . sysuse auto to load it.
. describe
gives an overview of a file. The command gives a count of the total number of observations in the file, and lists
each variable by: name, type, display format, value label and label information. If the file has been sorted, the sort
variable is identified.
. describe
Contains data from C:\Program Files\Stata10\ado\base/a/auto.dta
obs:
74
1978 Automobile Data
vars:
12
13 Apr 2007 17:45
size:
3,478 (99.7% of memory free)
(_dta has notes)
variable name
make
price
mpg
rep78
headroom
trunk
weight
length
turn
displacement
gear_ratio
foreign
Sorted by:
Note:
storage
type
str18
int
int
int
float
int
int
int
int
int
float
byte
display
format
%-18s
%8.0gc
%8.0g
%8.0g
%6.1f
%8.0g
%8.0gc
%8.0g
%8.0g
%8.0g
%6.2f
%8.0g
value
label
variable label
origin
Make and Model
Price
Mileage (mpg)
Repair Record 1978
Headroom (in.)
Trunk space (cu. ft.)
Weight (lbs.)
Length (in.)
Turn Circle (ft.)
Displacement (cu. in.)
Gear Ratio
Car type
make
dataset has changed since last saved
. summarize varname, detail
gives mean, std. dev., variance, skewness, kurtosis, and percentiles for the “varname” variable specified. Can be
abbreviated as: . su, . sum, . summ, etc.
. summ mpg, detail
Mileage (mpg)
1%
5%
10%
25%
50%
75%
90%
95%
99%
Percentiles
12
14
14
18
Smallest
12
12
14
14
20
25
29
34
41
Largest
34
35
35
41
Obs
Sum of Wgt.
74
74
Mean
Std. Dev.
21.2973
5.785503
Variance
Skewness
Kurtosis
33.47205
.9487176
3.975005
. codebook varname
gives much the same information as summarize, detail, except it includes the number of missing values, which is
extremely valuable information. Observations with missing values for variables used in a regression equation will
not be included. This is important to know early in a project and could have a huge impact on your analyses. To
get this information for ALL the variables in the file, simply issue the codebook command without specifying the
variable(s.)
6
. codebook mpg
mpg
Mileage (mpg)
type:
range:
unique values:
mean:
std. dev:
numeric (int)
[12,41]
21
units:
missing .:
1
0/74
21.2973
5.7855
percentiles:
10%
14
25%
18
50%
20
75%
25
90%
29
. tabulate varname
creates a frequency table of occurrences. This is useful for categorical variables or continuous variables with a
limited range of values. The by option is permitted. If the missing option is specified, missing values are included
in the frequency counts. To ask for more than one frequency table in a single command, use the .tab1 command:
. tab1, varone vartwo, etc..
. by foreign: tab mpg
-> foreign = Domestic
Mileage
(mpg)
Freq.
Percent
Cum.
12
14
15
16
17
18
19
20
21
22
24
25
26
28
29
30
34
2
5
2
4
2
7
8
3
3
5
3
1
2
2
1
1
1
3.85
9.62
3.85
7.69
3.85
13.46
15.38
5.77
5.77
9.62
5.77
1.92
3.85
3.85
1.92
1.92
1.92
3.85
13.46
17.31
25.00
28.85
42.31
57.69
63.46
69.23
78.85
84.62
86.54
90.38
94.23
96.15
98.08
100.00
Total
52
100.00
Mileage
(mpg)
Freq.
Percent
Cum.
14
17
18
21
23
24
25
26
28
30
31
35
41
1
2
2
2
3
1
4
1
1
1
1
2
1
4.55
9.09
9.09
9.09
13.64
4.55
18.18
4.55
4.55
4.55
4.55
9.09
4.55
4.55
13.64
22.73
31.82
45.45
50.00
68.18
72.73
77.27
81.82
86.36
95.45
100.00
Total
22
100.00
-> foreign = Foreign
7
. tabulate varone vartwo
creates a crosstab table. This is useful for categorical variables.
. tab rep78 foreign
Repair
Record
1978
Car type
Domestic
Foreign
Total
1
2
3
4
5
2
8
27
8
2
0
0
3
9
9
2
8
30
17
11
Total
47
21
68
. tabstat varone vartwo, etc.
is a more advanced alternative to the tabulate command. Tabstat produces nice tables for multiple variables,
including missing variables. Desired statistics can be specified, and the by option is permitted.
. tabstat price weight mpg rep78, by(foreign) stat(mean median sd variance count) missing
Summary statistics: mean, p50, sd, variance, N
by categories of: foreign (Car type)
foreign
price
weight
mpg
rep78
Domestic
6079.627
4749
3127.482
9781143
51
3309.804
3350
700.2614
490366
51
19.90196
19
4.759222
22.6502
51
3
3
.8340577
.6956522
47
Foreign
6384.682
5759
2621.915
6874439
22
2315.909
2180
433.0035
187492
22
24.77273
24.5
6.611187
43.70779
22
4.285714
4
.7171372
.5142857
21
.
5705
5705
.
.
1
3690
3690
.
.
1
16
16
.
.
1
4
4
.
.
1
Total
6165.257
5006.5
2949.496
8699526
74
3019.459
3190
777.1936
604029.8
74
21.2973
20
5.785503
33.47205
74
3.405797
3
.9899323
.9799659
69
8
. histogram varname
useful for checking that data is normally distributed and to look for outliers. By default, the histogram is scaled to
density units, i.e., the sum of their areas is equal to 1. Alternatively, frequency, fraction or percent scales can be
specified. The discrete option tells Stata to give each value of varname its own bar. The by option is permitted.
. histogram mpg, frequency discrete by(foreign, col(1))
0
8
Foreign
0
2
4
6
Frequency
2
4
6
8
Domestic
10
20
30
40
Mileage (mpg)
Graphs by Car type
. graph box varname
produces a box plot, another way to visualize the data that provides more information than a histogram. Adjacent
values are the 25th or 75th percentiles ± 1.5 times the Inter-Quartlile range (the distance between the 25th and
75th percentiles.) However, the adjacent values indicated on the graph, by convention, is "rolled back" to actual
data points, so they always reflect real data
9
o
o
adjacent line
<- outside values
<- upper adjacent value
whiskers
<- 75th percentile (upper hinge)
box
<- median
<- 25th percentile (lower hinge)
whiskers
adjacent line
<- lower adjacent value
o
<- outside value
10
. tsline varname
4000
3800
3600
3400
Calories consumed
4200
4400
plots time series data. (Type . sysuse tsline2 to load the example dataset.) This command must be preceded by
the . tsset command, which declares the data to be time-series. (See “Time Series” in “Creating & Modifying
Variables” for more information.) Like the histogram and the box plot graphs, the time series graph can be used to
look for anomalies and outliers. In the graph below, the spikes in calorie consumption at the end of the year are
probably holiday eating, but the data should be checked.
01jan2002
01apr2002
01jul2002
Date
01oct2002
01jan2003
Another way to check for outliers is to see if there are values that lie more than three standard deviations away
from the mean. This involves the use of the r(mean) scalar. To load the scalar with the mean for the variable that
is being checked, submit the . summarize varname, detail command, then list the observations that exceed ±
three standard deviations. Here are the commands and the results for the calories consumed datafile. The
excessive calorie consumption did occur during the holidays and therefore are probably not clerical errors.
. su calories, detail
. l if calories >=r(mean) + 3*r(sd) & calories < . | calories <=r(mean)-3*r(sd)
332.
359.
362.
365.
day
calories
ucalor~s
lcalor~s
28nov2002
25dec2002
28dec2002
31dec2002
4163.4
4340.2
3896.8
3898.2
4263.4
4440.2
3996.8
3998.2
4063.4
4240.2
3796.8
3798.2
11
VERIFYING THE DATA
. assert
is useful for checking data, particularly in do files, where many assets can be listed back to back to check that the
data really are as expected. If assertions are true, Stata quietly goes through the do file executing the commands.
If an assertion is false, Stata aborts operation and gives an error message that includes the number of instances
where the assertion is false.
. assert gender==0 | gender== 1
2 contradictions in 365 observations
assertion is false
r(9);
To see the contradictions, use the list command qualified by an if statement.
. list if gender !=0 & gender
18.
32.
!=1
day
calories
ucalor~s
lcalor~s
gender
18jan2002
01feb2002
3679
3582
3779
3682
3579
3482
.
11
.assert is also helpful after two files have been merged to see if there are non-matches. A merge value equal to 1
(_merge==1) means that the data came from the “master” file only; _merge==2 means the data came from the
“using file”; a _merge==3 means both files contributed data to the observation.
.assert _merge==3
More information can be found in the section on “Modifying & Combining Files.”
NUMERIC MISSING VALUES
As explained in the description of the . codebook command, careful consideration must be paid to missing values
in a datafile. By default, Stata uses a “.” for numeric missing values. Internally, “.” is stored as a very, very large
number. Most commands, like . regress, drop observations with missing values, which can cause problems if too
many observations are dropped from the analysis. Just as important, other commands, like . replace, do NOT
ignore them. The command .replace group=3 if group>=3 will replace any group with a missing value with a 3.
To exclude missing values from the replacement, the command must be written as:
. replace group=3 if group>=3 & group <.
The .egen rmiss() function can be used to create a new variable that stores a count of the number of missing
numeric values in each observation. An extension to the egen command rmiss2() counts the number of missing
values in both numeric and string variables. It can be downloaded by typing the . findit rmiss2.
The . tabmiss program creates a frequency table of the number of missing values by variable. The program
works for both numeric and string variables. , It can be downloaded by typing, findit tabmiss.
See: http://www.ats.ucla.edu/stat/Stata/faq/nummiss_stata.htm for more information about tabmiss and rmiss2().
12
. correlate - CHECKING FOR CORRELATION
Checking pairwise correlations gives an overview of the relationship between the numeric variables in a study.
Looking at the data at this level can provide useful information for regression model specification.
. correlate varone vartwo
A scatterplot with regression line graph can also be informative. See:
http://www.ats.ucla.edu/stat/stata/faq/scatter.htm
13
CREATING & MODIFYING VARIABLES
CREATING NEW VARIABLES
Variable Names:
1) Variable names are case-sensitive. Total, TOTAL and total are seen as three different variables by
Stata. So, it is important to be consistent.
2) Names must begin with a letter.
3) Spaces are not allowed; use an underscore instead.
4) Names can be up to 32 characters in length. Shorter names (eight characters or less) are preferred,
because longer names are often truncated in result outputs.
= Versus ==
A single equal sign, =, is used in mathematical expressions. Use = with the . generate, . rename and . recode
commands.
A double equal sign, ==, is a logical operator that returns a 0 if an expression is false and a 1 if it is true. Use ==
with an if qualifier to test for equality. Examples include:
. regress price mpg foreign if foreign == 1
. su price if region == “West”
(Recall: su, sum, summ are all equally valid abbreviations for the . summarize command.)
. generate
To create a new variable, use the . generate (. gen) or . egen command. Spacing is not important; operators can
have spaces before and/or after or none at all. Constants and variables can both be used to create new variables.
For a complete list of operators, type: . help operators.
Addition/Subraction
. gen sum = varone + vartwo
. gen sum2=varone + 10 // constants can also be used to create variables
. gen net_pay = gross – deductions
Division /Multiplication (Interaction)
. gen total = price * quantity // total is equal to price times quantity
. gen mpg=mileage/gallons // mpg is equal to mileage divided by gallons
Exponentiation
. gen x_sq = x^x OR . gen x_sq = x*x
. gen x_cubed = x^3 OR . gen x_cubed=x*x*x
14
Lag
Useful with time-series data.
. gen price_lag = price[_n-1]
// price_lag equals the price in the previous observation
_n is a system variable that refers to the current observation. _n-1 refers to the previous observation. For a
complete list of system variables, type: .help _variables in the command window.
Note: If the dataset has been declared to be time-series with the . tsset command, then the . L.varname
command can be used in a regression equation. This method works without creating new variables. See “Time
Series” in “Creating & Modifying Variables” for more information.
Log:
Often used when the relationship between the dependent variable and the independent variable is not constant.
. gen log_x = ln(x)
. egen
The egen command provides extensions to the generate command and offers very powerful capabilities for
creating new variables with summary statistics. Egen works by observation (row) or by variable (column.) In the
next two commands the new variables will have values based upon other variables in the same observation.
. egen AvgScore=rowmean(test1 test2 test3 test4)
. egen answered=rownonmiss(question1 – question25)
In the next command, a summary statistic for a column of data will be added to each observation. The same value
will be added, unless subsets are created using the by option.
. egen subtotal = total(price)
DUMMY VARIABLES
There are a number of different methods that can be used to create dummy (indicator) variables which take on a
value of 0 if the condition evaluates to false, and 1 if it is true.
The easiest way ONLY works if there are no coding errors, so it is important to check first using either the
. tab varname (tabulate) or .assert commands.
The Easy Way
For a variable such as “educat” with five categories:
1) no education completed
2) primary completed
3) secondary completed
4) tertiary completed
5) technical/vocational
First, confirm that the variable really does have only those five categories .
. tab educat, missing
Second, use the following command to create the five dummy variables which by default will be named:
educat_1, educat_2, educat_3, educat_4, educat_5; one for each of the “educat” categories.
15
. tab educat, gen(educat_)
Finally, . rename the dummy variables to something more descriptive. It is crucial to know EXACTLY how the
“educat” categories were coded. If they are coded “0” for “No education”, “1” for “Primary completed”, “2” for
“Secondary completed”, “3” for “Tertiary completed”, and “4” for “Technical/vocational education completed”,
then issue the following (five) commands to rename the variables:
. rename educat_1 Noedu
. rename educat_2 Pri
. rename educat_3 Sec
. rename educat_4 Ter
. rename educat_5 Voc
However, if they were coded “0” for “No education”, “1” for “Technical/vocational education completed”, “2” for
“Primary completed”, “3” for “Secondary completed”, and “4” for “Tertiary completed”, then you would issue the
following commands:
. rename educat_1 Noedu
. rename educat_2 Voc
. rename educat_3 Pri
. rename educat_4 Sec
. rename educat_5 Ter
Note how the subsequent analysis would be completely thrown off by a renaming mistake. If the dummies were
renamed the first way when the second way really corresponded to the coding of the underlying “educat” variable,
then we would effectively mistake tertiary education with technical/vocational education and vice-versa – not
good if doing a wage regression!!!
Second Method
This way is more tedious but it is also more foolproof than the first approach. It’s foolproof because it explicitly
takes into account the EXACT definition of the underlying variable from which the dummy variables were
created. The dummy variables are created one at a time. Assuming that the coding of “educat” follows the first
convention explained in the first method, issue the following commands to create the dummy for “Noedu”: The
dummies for “Pri” through “Voc” are created using the same approach.
generate Noedu = .
replace Noedu = 0 if educat >=0 & educat <= 4
replace Noedu = 1 if educat==0
The first command creates the new variable, Noedu, initially with “missing” values.
The second command “fills up” the missing values with zeros – note: the if qualifier explicitly takes the valid
range of the “educat” variable into account.
Finally, the third command replaces the zeros with ones when the if qualifier evaluates to 1 (true.) Otherwise, the
zeros are left intact.
Third Method
The third method involves the use of the interaction expansion (. xi) command.
.xi: regress wage agegrp i.educat i.race
As in the first method, dummy variables are created automatically. By default, the names assigned to the dummy
variables start with _I. To see their definitions, type:
.describe _I*
For more information, by typing . help xi in the command window.
16
DATES
Any model that involves time-series data or date manipulation, such as generating an elapsed time variable, needs
its date variable(s) in Stata date format. The base date used by Stata is January 1, 1960, which is assigned a value
of 0. Dates prior to 1/1/1960 have negative values while dates post 1/1/1960 have positive values. Two functions
are provided that will do the conversions, which to use depends upon how the dates in the file are formatted.
If there are separate variables for month, day and year, then use an mdy type function:
. gen birthday=mdy(month, day, year)
If the date is a string variable, such as: Jan 1, 2008, then use the date function:
. gen birthday=date(datevar, “MDY”)
Once created, the date variable can be formatted to look more “intelligible” to humans. The format command
causes a Stata date such as 17532, (17532 being the number of days since Jan 1, 1960,) to be written as 1jan2008.
. format birthday %td
For more information about this subject, see Stata’s .help.
MODIFYING VARIABLES
String Variables
In Stata, non-numeric variables are called “string” variables. To specify a particular string value, enclose it in
double quotes. Remember, since Stata is case-sensitive, each of the following are evaluated as unique strings.
Also note the use of the double equal sign; the if qualifier is testing for equality.
if gender == “male”
if gender == “Male”
if gender == “ male”
if gender == “male ”
if gender == “24450”
A string variable can consist solely of numbers, but mathematical operations cannot be performed with it.
Therefore it can be a good idea to format numbers for which a mathematical operation is inappropriate, such as
identification numbers, as string variables. See the help file for more information on the following two
approaches:
. tostring or
. generate as_str
. destring
If a variable was mistakenly imported as a string variable when it should have been numeric, the . destring
command will convert it. (Remember, the . describe varname command gives information on how a variable is
stored, and string variables are usually displayed as red in the Data Editor window.) Before converting the
variable to a numeric format, the input error that caused it to be stored as a string must be fixed. For example, if
the following set of numbers was imported as the variable batavg, the data will be stored as a string because of the
comma in the third observation.
.308
17
.272
,215
.299
To fix the observation, either use the Data Editor, or. replace, the next command to be described.
.replace batavg = “.215” in 3
After fixing the data entry error, the .destring command will convert the string to a numeric variable.. The safest
approach is to create a new variable using the generate option, and to check the results. The original variable can
be dropped and the new one renamed, once the accuracy of the conversion has been verified.
. destring batavg, generate(batavg_num)
. describe batavg_num
. drop batavg
. rename batavg_num batavg
. replace
This command is used to change the contents of a variable when the variable already exists. Because it is so
powerful, Stata makes you type out the entire command; there are no abbreviations. It is often used with the if
qualifier. The first example changes missing values that were coded as -999 to Stata’s “.” missing value. The
second corrects typos in a string variable.
. replace price = . if price == -999
. replace gender = “male” if gender == “mael”
. recode
This is a useful command to collapse a continuous variable into groups. While the . replace command can be
used, it is quicker with .recode. To be safe, generate a new variable with the same values as the variable to be
recoded, then work with the NEW variable, just in case a mistake is made. In the following commands categories
for the continuous variable “ age” are being created:
. gen AgeGrps=age
. recode AgeGrps (min/18=1) (19/21=2) (22/29=3) (30/49=4) (50/66=5) (67/max=6)
The recode command is also used to swap the values of a categorical variable. This is often done to match the
coding of similar variables so an index can be created. For example, if two questions have a value of “1” for
“Very Dissatisfied” and a value of “5” for “Very Satisfied.” but one question, quest3, has the coding reversed,
recode can be used to change the values in quest3 to match those in questions 1 and 2. In the example code, the
value of “3” remains unchanged, so it does not need to be specified. Again, for safety’s sake, work is being done
with a copy of the variable.
. gen quest3_recode=quest3
. recode quest3_recode = 1=5 2=4 4=2 5=1
18
MODIFYING AND COMBINING FILES
MODIFYING A FILE
. keep & . drop
The keep and drop commands can be used either on variables or observations. Often when importing data, there
are more variables then needed. Whether to use the . keep or the . drop command to get rid of the unnecessary
variables depends upon the number of variables you want to keep. If there are more variables that you do not
want, then use . keep. If however, there are only a few variables that you do not want, then use . drop.
. keep varlist
. drop varlist
To keep or drop an observation(s) based on a set of criteria, the commands are combined with the if qualifier.
. keep if mpg >= 25 & mpg < .
// missing values are being dropped
. drop if price < 10000
To drop specific observations, use the in qualifier. This command tells Stata to drop the first five observations.
. drop in 1/5
. move & . order : reordering the list of variables
The order of variables in the Variables window, and thereby in the Data Editor can be changed. In the examples
below, .move relocates varone to the position occupied by vartwo and shifts the variables below down one place.
The . order command places the variable(s) specified at the top of the variable list.
. move varone vartwo
. order newvar oldvar
. sort
By default, Stata sorts variables in ascending order, from smallest to biggest or from A to Z, based upon the
variables specified in the varlist. .gsort sorts in descending or ascending order. The stable option insures that the
observations stay in the same relative order that they held before the sort. Without this option, the order of
observations that have equal sort values is randomized.
/* Sorts observations in ascending order of mpg. Relative order of observations prior to the sort will be
maintained for observations with equal mpg values. */
. sort mpg, stable
//
. gsort +mpg, stable //equivalent to the previous command; mpg is sorted in ascending order.
//
/* Sorts observations in descending order based on mpg. Order of observations with equivalent mpg
values is randomized. */
. gsort –mpg
//
//* Sorts the variable make in ascending order (from A to Z) and then within each make, mpg is sorted in
descending order. */
. gsort make –mpg
19
. reshape (wide  long or long  wide)
Frequently time-series data is obtained in a “wide” format. The identifying variable is given and the time-series
data values (yearly, quarterly, monthly, etc.,) are spread along the columns of a single row. In many cases, the
data needs be rewritten so that the data values are separated into multiple rows. In this example, country gdp
values for the years 1980-2008 are separate variables along a single observation. (Aside: country is a string
variable which, by default, is red.)
To reshape the data, the identifying variable (i) and the stub name (j) must be determined. In this case, country is
the i variable and gdp is the “stub name” (gdp1980, gdp1981, etc.) The command becomes:
. reshape long gdp, i(country) j(year)
and it issues this result:
(note: j = 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
> 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008)
Data
wide
->
long
Number of obs.
172
Number of variables
30
j variable (29 values)
xij variables:
gdp1980 gdp1981 ... gdp2008
->
->
->
4988
3
year
->
gdp
The number of observations went from 172 to 4988 and there are now 3 variables when there were 30 when the
data were “wide.” Issue either the .edit or . browse command to open the Data Editor. Here’s a snapshot of a few
of the observations:
20
Here’s the command that would change the data back from “long” to “wide”:
. reshape wide gdp, i(country) j(year)
and the result:
. reshape wide gdp, i(country) j(year)
(note: j = 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
> 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008)
Data
long
->
wide
Number of obs.
Number of variables
j variable (29 values)
xij variables:
4988
3
year
->
->
->
172
30
(dropped)
gdp
->
gdp1980 gdp1981 ... gdp2008
It is possible to have multiple identifying variables, e.g. state and city, and the j variable can be a string. If Stata
detects any mistakes in the data that affect its ability to execute the reshape command, an error message instructs
you to type “reshape error” to get the details.
COMBINING FILES
There are three ways to combine files. Additional observations from one datafile can be added to the end of
another with the .append command. Additional variables contained in one file can be added to corresponding
observations in another with the .merge command. The .joinby command works on groups and only keeps
observations that are found in both files. All three methods use a similar approach. The datafile in memory (the
one that is currently open,) is referred to as the “master” file. The file that is to be joined with the “master” is
known as the “using” datafile. Both files must be Stata files.
. append
Append adds observations from the using file to the end of the master file. The files are stacked vertically.
. append fileinmemory using AddObs
. merge
Merge adds additional variables to observations. If the two datafiles are in EXACTLY the same order, a
matching variable contained in both files is not needed. But usually, both files have a matching variable (or
variables) that is used to associate an observation from the master file with an observation in the using file. Before
files can be merged, they must be SORTED.
Merge creates a system variable with the name, _merge. After a merge, the . tabulate command can be used to
get a frequency table of the results of the merge. The _merge variable has five possible values, but in most cases,
unless the using file is being used to update the master file, the first three are the values of interest:
_merge==1
_merge==2
_merge==3
observation found in master file only
observation found in using file only
observation built from both master & using files. Normally, this is the desired value.
There are two types of merges. In a one to one match, each observation in the master file has a corresponding
observation in the using file. In a one to many merge, the using file may have multiple observations per
observation in the master file. Both types of merges work in the same manner. Here is some example code that
could be saved in a do file to accomplish a one to many merge:
21
version 10.0
* Load, sort and save master file
use patient.dta, clear
sort id
save patient.dta
//
* Load, sort and save “using” file
use visits, clear
sort id, stable // stable option maintains the relative order of observations
. save visits
//
* Reload master file and merge with using file by id
use patient.dta, clear
merge id using visits // joins observations in the patient file with obs in the visit file by id.
//
tab _merge // Check to see how the merge worked. (tab is an abbreviation for . tabulate.)
drop _merge // If the merge worked as planned, the _merge variable should be dropped.
save AllInfo, replace
TIME SERIES AND PANEL DATA
A data file that contains observations on a single entity over multiple time periods is known as time series data. If,
as in the gdp example above, there is a group for whom there is data over a number of time periods, then it is
known as panel data. To use Stata’s time-series functions and capabilities, the dataset must be declared to be
time-series data. There are a number of items that need to be accomplish before that can be done. First, the dataset
must contain a date variable and it must be in Stata date format. Second, the data must be sorted by the date
variable. If it is panel data, the data must be sorted by date within the panel variable(s). Finally, Stata must be told
that the file contains time-series or panel data with the . tsset command.
Stata can be used to create a time variable for a dataset that does not have one, but the data must be in the correct
time sequence and there can NOT be any gaps in the time series. To create a year variable that begins in 1975,
issue the commands:
. generate year = 1975 + _n-1
To generate a monthly variable beginning in July of 1975 for a panel dataset, the commands would be:
. sort country
. by country: generate time=m(1975m7) +_n-1
. format time %tm
To declare a file with yearly data to be a time-series dataset and then to plot the data, issue these commands:
. sort year // Data must be sorted before the . tsset command can be used.
. tsset year, yearly // Declares data to be time series.
. tsline gdp // Plots the time series data. See “Examining the Data.”
Stata’s response would be:
. tsset year
time variable:
delta:
year, 1980 to 2008
1 year
To . tsset a panel data file, that contains gdp values for multiple countries, the commands would be:
22
. format year %ty
. sort country year
. tsset country year, yearly
. xtline gdp // Plots the a time series graph for each country in the dataset.
Note, the panel variable comes before the time variable and unfortunately, it must be numeric ! So the above
commands would not work if the country variable is a string variable. First, a numeric id number for each country
must be generated.
This code will generate an id number for each country and then declare that the file contains panel data.
. sort country year
. egen cntryid=group(country) // This assigns the values of 1, 2, 3, etc to the various countries.
. tsset cntryid year, yearly
Here are the results:
. tsset cntryid year, yearly
panel variable: cntryid (strongly balanced)
time variable: year, 1980 to 2008
delta: 1 year
Frequently, in time series analysis, it is desirable to “lag,” “lead,” or compute the difference between the value of
a variable and adjacent observations. Since the data has been declared to be a time series, the time series
operators, (L., F., and D,) can be used. There are several advantages to this approach, the most important being
that it is less error prone than other techniques. Another positive is that this method does not created new
variables.
Time Series Operators

L. is equivalent to x[_n-1], i.e., the value of x in the previous observation.

L2. is the value of x two time periods in the past. (The lag can be any desired amount.)

F. is equivalent to x[_n+1], i.e., the value of x in the next observation.

D. is equivalent to the difference between the current value and the previous value.
All these commands follow the same syntax, so just a lag example will be demonstrated. In a regression equation,
specify the variable to lag or lead and the number of time periods desired.
. regress wage L.wage L.2wage
The command to clear time series settings is . tsset, clear.
23
REGRESSION ANALYSIS
OLS (Studenmund, pp. 35 – 40)
. regress Y X1 X2 X3
To predict residuals and save the results as "Residuals,” use the command:
. predict Residuals, resid
To plot residuals together with suspected proportionality factor (to detect heteroskedasticity):
. twoway scatter Residuals X3
Restricting Stata commands to observations with certain conditions satisfied
You may want to calculate descriptive statistics and/or estimate a regression, say, for the full sample, as well as
for females and males separately (micro data) or full sample, OECD-countries, Non-OECD countries, SubSaharan African Countries (macro data).
One thing to notice here, is that when it comes to conditions like these, Stata needs two equality signs, rather than
one – for the previous two examples, say:
.summarize Y X1 X2 X3
. summarize Y X1 X2 X3 if X3==1
. summarize Y X1 X2 X3 if X3==0
. regress Y X1 X2 X3
. regress Y X1 X2 X3 if X3==1
. regress Y X1 X2 X3 if X3==0
//
. summarize Y X1 X2 X3
. summarize Y X1 X2 X3 if oecd==1
. summarize Y X1 X2 X3 if oecd==0
. summarize Y X1 X2 X3 if ssa==1
. regress Y X1 X2 X3
. regress Y X1 X2 X3 if oecd==1
. regress Y X1 X2 X3 if oecd==0
. regress Y X1 X2 X3 if ssa==1
Note that in the regression-case, it does not matter whether the variable, you restrict upon, is included as an
explanatory variable or not – Stata will detect that it is collinear (since there will be no variation in it) and
automatically exclude it from the explanatory variables.
Ensuring Consistency Among the Number of Observations in the various estimation samples
In descriptive analyses, the number of observations often differs across the variables. Similarly, the number of
observations will likely differ for regressions using various specifications of explanatory variables. This is due to
some observations having missing values for some variables but not for others, thus creating “holes” in the
number of observations.
You want:
1) The number of observations for each variable within the descriptive analysis to match up.
2) The number of observations used in the different regression specifications to match up.
24
3) The number of observations between (1) and (2) to match up.
To ensure this, do the following:
1) Load your Stata dataset.
2) Run the model specification that gives rise to the biggest drop in sample size:
. regress Y X1 X2 X3 X4 X5 X6 X7 X8 X9 X10
3) Use . keep to retain only the observations that have non-missing values for all variables, dependent
and explanatory, from the above regression:
. keep if e(sample)
4) Stata will then drop all the observations that have one or more missing (dependent and/or
explanatory) variables – so that all subsequent analyses will be performed on one, consistent dataset.
Ramsey’s Regression Specification Error Test (RESET) (Studenmund, pp. 198 - 201)
Run the regression of interest, then:
. regress Y X1 X2 X3
. estat ovtest
Akaike’s Information Criterion and the Schwartz (Bayesian Information) Criterion (Studenmund, p. 201)
Run the regression of interest, then:
. regress Y X1 X2 X3
. estat ic
Detection of Multicollinearity: Variance Inflation Factors (Studenmund, p. 259)
Run the regression of interest, then:
. regress Y X1 X2 X3
. estat vif
SERIAL CORRELATION
In order to use any time-series analysis related commands in Stata, including running a DW test, you must first
define the dataset to be a time-series dataset (if this has not already been done). Tell Stata which variable defines
the time dimension (here, “qtrs.”) For more information about the .tsset command, see “Time Series” in the
previous section (Modifying & Combining Files.)
Durbin-Watson D Test for First-Order Autocorrelation (Studenmund, p. 325)
If it has not been done already, tell Stata it is dealing with time series data, run the regression of interest then
check for serial correlation:
. tsset qtrs
. regress Y X1 X2 X3
. estat dwatson
25
Dealing With Autocorrelation
GLS Using Cochrane-Orcutt Method (Studenmund, p. 332)
Letting Y be the dependent variable and X1, X2, and X3 explanatory variables, issue the command:
. prais Y X1 X2 X3, corc
Newey-West standard errors (Studenmund, p. 334)
Again letting Y be the dependent variable and X1, X2, and X3 explanatory variables, issue the command:
. newey Y X1 X2 X3, lag(#)
where “lag(#)” specifies the order of the autocorrelation – again, we will typically think only first-order
autocorrelation is relevant, and therefore specify “lag(1)” but if you think second-order autocorrelation might be
relevant, you could go on to specify “lag(2).”
HETEROSKEDASTICITY
White test (Studenmund, p. 360)
Run the regression of interest, then:
. regress Y X1 X2 X3
. estat imtest, preserve white
Weighted-Least Squares (WLS) (Studenmund, p. 363)
At the time of writing, Stata does not contain a direct WLS procedure but you can download the user-written
“wls0” command and then use that from within Stata (see
http://www.ats.ucla.edu/stat/stata/ado/analysis/wls0.htm for more details and an example.)
Heteroskedasticity-corrected (HC) standard errors (Studenmund, p. 365)
Run the regression of interest, specifying the “robust” option:
. regress Y X1 X2 X3, robust
26
HYPOTHESIS TESTING
t-tests
The results (t-statistics and the p-value of the t-statistics) here are automatically provided – for the two-sided
alternative if you want to test one-sided, you’ll need to divide the p-value by two – AND check that the sign is in
the expected direction!!
f-tests
Again, the t-test is a partial test, i.e., it tests for the statistical significance of each individual regressor/explanatory
variable in turn If we want to perform tests that involve a group of variables, we need something else – one
possibility is the F-test.
There are two “flavors” to consider:
Tests for joint statistical significance of explanatory variables
1) Run the regression of interest, say:
. regress Y X1 X2 X3 X4 X5
2) Perform (an) F-test(s) of the group(s) of explanatory variables, you are interested in, say:
. test X1 X2
and/or:
. test X1 X2 X3
Stata will give you the F-Statistic and its associated p-value for a test of the null hypothesis of lack of joint
statistical significance and the alternative hypothesis of joint statistical significance for the group of explanatory
variables as a whole (recall the decision rule for statistical tests when using the p-value!)
Testing for equality of coefficients
Alternatively, we may be interested in testing whether the coefficients for a group of explanatory variables are
equal. For example, whether the coefficients for a set of dummy variables for different ethnicities in a wage
regression are the same.
1) Run the regression of interest, say:
. regress Y X1 X2 X3 X4 X5
2) Perform (an) F-test(s) of the group(s) of explanatory variables, you are interested in testing for
identical coefficients – say:
. test X1 = X2
and/or:
. test X1 = X2 = X3
Here, the null hypothesis is that all the coefficients are equal to each other and the alternative hypothesis is that at
least one of the coefficients is different from the other coefficient(s).
27
MAKING “NICE” TABLES OF REGRESSION RESULTS
The default in Stata is to report results in a “wide” format, i.e., the estimated parameters in the first column, the
estimated standard errors in the second column, etc. There are two different approaches to creating “nice” tables –
similar to how results are reported in journal articles, i.e., with the standard errors below the parameter estimates,
along with fit measures (R2, adjusted R2, Akaike Criterion, etc.,) and the number of observations, etc., at the
bottom.
The first gives you the brackets around the standard errors, as in most journal articles. The second is more
comprehensive, allowing you to include t-statistics, p-values, several different fit measures (rather than merely
R2), etc., but you have to make the brackets around the standard errors yourself. The two methods work as
follows:
. outreg2
1) This is a user-written command, so first you need to install it from the Stata collection at Boston
College:
. ssc install outreg2, all replace
2) Run the first regression that you want to be part of the table – and then save the results in a text-file,
specifying the path where you want to save it, replacing any existing file with the same name:
. regress Y X1 X2 X3
. outreg2 using “C:\capstone\result_tbl_1.out”, nolabel bdec(3) coefastr se bracket 3aster replace
3) Run the additional regressions that you want to be part of the table, appending them to the previous
results:
. regress Y X1 X2 X3 if X3==1
. outreg2 using “C:\ capstone\result_tbl_1.out”, nolabel bdec(3) coefastr se bracket 3aster append
. regress Y X1 X2 X3 if X3==0
. outreg2 using “C:\ capstone\result_tbl_1.out”, nolabel bdec(3) coefastr se bracket 3aster append
4) Import the text-file into Excel, to create the table, then copy and paste into Word.
THE “ESTIMATES STORE” AND “ESTIMATES TABLE” COMMANDS:
1) Run the regression(s), that you want to be part of the table, saving the results after each regression:
. regress Y X1 X2 X3
. estimates store model1
. regress Y X1 X2 X3 if X3==1
. estimates store model2
. regress Y X1 X2 X3 if X3==0
. estimates store model3
2) Create the table, by calling the model results, that you saved previously, and add the statistics, etc,
you want to include (here, we ask for coefficients, standard errors, t-statistics, p-values + R2,
adjusted-R2, the Akaike Information Criterion, the Bayesian Information Criterion, and the number of
observations) :
. estimates table model1 model2 model3, stats(r2 r2_a aic bic N) b(%7.3g) se(%6.3g) t(%6.3g) p(%4.3f)
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NOTE: if there is some parts of the previous table, you don’t want to include, you just modify the
previous Stata command accordingly. Say, you are not really interested in the t-statistics or p-values
but only want to include the coefficients and their standard errors (+ the fit measures and N from
before):
. estimates table model1 model2 model3, stats(r2 r2_a aic bic N) b(%7.3g) se(%6.3g)
Note that if you want “stars” to indicate the level of statistical significance, you CANNOT combine
this with the “se”, “t”, and/or “p” options from above. An example on getting “stars” in the fashion
known from economic journals, etc, similar to the previous, is:
. estimates table model1 model2 model3, stats(r2 r2_a aic bic N) b(%7.3g) star(0.1 0.05 0.01)
3) Highlight the table (i.e. from top edge of table down to and including the bottom line of the table – do
NOT highlight the legend, that will mess up the formatting in Excel subsequently!!) in the results
window with the cursor, right-click on it and chose “Copy Table” (NOT “Copy Text”) and then copy
and paste into Excel, to create the table, then copy and paste into Word.
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MORE ADVANCED REGRESSION MODELS
. logit & . probit models
Relevant whenever the dependent variable is a binary (i.e. “dummy” or (0,1) variable). Consult an econometrics
textbook for details!!
In Stata, type, say (for the probit and logit, respectively):
. probit Y X1 X2 X3
. logit Y X1 X2 X3
Issue: since the logit and probit models are non-linear in the estimated parameters (unlike the OLS model), the
estimated coefficients are not directly interpretable as marginal effects (again, unlike in the OLS model). The
reason for this is that due to the non-linearity, the marginal effects of a given explanatory variable will depend on
the value of ALL other explanatory variables, as well. We therefore need to evaluate the marginal effects of a
given explanatory variable at some value of the other explanatory variables. Typically we’ll set all the other
explanatory variables at their mean value.
To run logit and/or probit models in Stata, where the results yield marginal effects type, say:
Probit with marginal effects reported
. dprobit Y X1 X2 X3
Or:
1) Run the probit of interest:
. probit Y X1 X2 X3
2) Calculate the marginal effects, evaluated at the means of all other explanatory variables:
. mfx
Logit with marginal effects reported
1) Run the logit of interest:
. logit Y X1 X2 X3
2) Calculate the marginal effects, evaluated at the means of all other explanatory variables:
. mfx
Multinomial logit
Relevant whenever the dependent variable is a qualitative variable with more than two outcomes that cannot be
ordered/ranked (if it could be ordered/ranked, we would use, for example, an ordered probit – and if it had only
two outcomes, we would use instead the (simple) probit and/or logit model, discussed previously.) Examples
include transportation choice (car, bus, train, bike, etc), health provider (doctor, healer, etc).
NOTE: Consult an econometrics textbook for details!!
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In a do file, type:
mlogit Y X1 X2 X3 X4 X5, basecategory(#)
mfx, predict(outcome(#))
mfx, predict(outcome(#))
mfx, predict(outcome(#))
mfx, predict(outcome(#))
where Y is the dependent variable and the Xs are the explanatory variables.
The "#" after “basecategory” sets the category you want to be the reference category (all results are relative to this
category). Which one you choose matters mostly for the interpretation, although it is better to have a reference
group with a relatively high number of observations (yields relative more precise estimates for the estimated
parameters (i.e. the coefficients), since the coefficients, again, are relative to the base category).
Again we have a problem with the estimated coefficients not being interpretable as marginal effects (since the
multinomial logit model is non-linear in the estimated parameters – as were also the (simple) logit and probit
models from before) – and again the “mfx” command calculates the marginal effects (again, here these are to be
interpreted as the marginal probability, ceteris paribus, for the outcome in question – since the dependent variable
is qualitative). Again, we will typically set all the other explanatory variables at their mean value (which is also
the default for the “mfx” command).
Aside: because marginal effects add up to one, unlike what was the case for the coefficients, we can calculate
marginal effects for ALL outcomes (the "#" after “outcome” refers to the particular outcome of the dependent
variable for which you want to calculate the marginal effects.
If you want to know more about the command, type . help mlogit in Stata.
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APPENDIX A
DO FILE EXAMPLE
* Note: actual commands are in bold face type and all 3 types of comment markers are used as examples.
/* Three good commands to include no matter what the rest of the program is doing. */
version 10.0
/* Set the version of Stata (you can see the current version number by typing “version” in Stata’s command
window.) (NOTE: Sometimes user-written commands may work only under an older
version of Stata, say, version 9 – in that case, one would type “version 9.0” below, instead)
*/
capture log close // Close any open log-files.
clear
// Clear memory allocated to Stata in order to start with a clean slate.
/*
The next two commands may be necessary when working with large datasets.
The amount of memory that can be assigned depends upon the memory of the machine in use and the number of
other programs that are running.
*/
set memory 300000k // increase memory assigned to Stata.
set matsize 200
// increase the number of parameters allocated for dataset
/*
Open a log file to document the session. Specify the relevant path rather than the “X”s, “Y”s, and “Z”s. The
replace option permits overwriting of a file with the same name.
*/
log using "C:\XXXX\YYYYY\ZZZZZ.log", replace
/*
Load the Stata file by specifying the complete path rather than the “X”s, “Y”s, and “Z”s.
There are many methods for creating a Stata datafile: copying and pasting, data entry in the Data Editor, and the .
insheet command are a few examples.
*/
use " C:\XXXX\YYYYY\ZZZZZ.dta", clear
/*
Get to know the data, run a regression, save residuals and plot residuals to look for heteroskedasticity.
*/
summarize // Get basic descriptive statistics for all variables. Specify specific variables, if desired.
histogram X1
// Check variable X1 for outliers.
graph box X2
// Create a box plot for variable X2 to check for outliers.
twoway scatter X1 X2 // Create scatter plot with X1 on Y axis and X2 on X axis
correlate X1 X2
// Estimate the partial correlation between X1 and X2
regress Y X1 X2
// Estimate an OLS regression of Y on X1 and X2
predict Residuals, resid // Predict residuals and save as a new variable called, "Residuals"
twoway scatter Residuals X3 /* Plot residuals with suspected proportionality factor, X3,
(to detect heteroskedasticity)
*/
Wrap up work: save file with new name, close log file and exit do file.
*/
save " C:\XXXX\YYYYY\newfile.dta" // Save file with Residuals variable under new name, if desired.
capture log close // Close log file.
exit
// Return control to the operating system, not necessary, but good practice.
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APPENDIX B
IMPORTING AN EXCEL FILE INTO STATA
1. Excel file
a. Delete titles and footers
b. Assign short variable names (8 characters), NO SPACES
i. Names can be as long as 32 characters, but long names will likely be truncated when
displayed and that can cause identification problems.
ii. Use an underscore rather than a space, e. g., net_inc
c. Save as *.csv
2. Stata
a. . insheet using filename, comma names case
i. comma option is not necessary, but it speeds things up.
ii. names option tells Stata that variable names are included in the file, which on occasion
the program does not recognize.
iii. case option instructs Stata to keep variable names in the case specified in the spreadsheet,
otherwise variable names become all lower case.
b. open Data Editor to look at file; by default, string variables are red and numeric variables are
black
c. . count, . codebook, .describe, .summarize commands may be useful
3. Destring command to change a variable from string to numeric
a. sometimes coding errors can cause numeric variables to be imported as string variables.
b. examples include double periods “..” or commas “,,” in a column.
c. . destring varname, replace ignore(".." ",,") fixes this situation.
d. The ignore option changes the “..” and to “.” and makes the variable a numeric.
4. Changing missing values to Stata missing value codes.
a. many data files use “-9” to indicate a missing value; this is NOT a missing value in Stata
b. Stata uses “.” (and .a, .b, ... .z for more extensive tracking of missing values)
c. “.” is the value to use
d. . recode varlist ( -9 = .)
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APPENDIX C
DOWNLOADING DATA FROM ICPSR
Washington & Lee is a member institution of ICPSR, so we can download files from that site. You will be asked
to log in, or if you are a first time user, to register, when you try to download data.
The download process has five steps:
Step 1:
 If there is a Stata DTA option, select it.
 If there isn’t a *.dta file, select the ASCII Data File + Stata Setup Files option if one exists.
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If neither a *.dta nor a Stata setup file is available, ask Carol Karsch, the Data & Statistical Support
Specialist at Leyburn Library. She can work with both SAS and SPSS.
Step 2: From the list of available datasets, select the item(s) of interest
Step 3: Add the data to the cart
Step 4: Review the cart (if desired.)
Step 5: Download the cart
You’ll be sent a “zipped” folder that needs to be decompressed before you can work with it. Usually, there is a
“Codebook” , a “descriptioncitation” which gives a brief synopsis of the study’s methodology, a manifest, which
gives technical details, a related_literature document, in addition to the data and setup files.
The codebook should be carefully read before work with the data begins. A codebook describes the study. It
gives details about the data that are extremely useful. If the variables in the dataset are not labeled, the codebook
is indispensable. The codebook describes the variables, their locations in the file, what the values mean and
potential information about the variables that is necessary to using them correctly. It helps the researcher verify
that the data will be useful for the purpose intended. It may not! The data might not have the level of detail
required. If the analysis is a time series, the questions important to the research may not be asked in the years
under consideration. Groups important to the analysis maybe dropped because of privacy issues. A careful
reading of the codebook(s) can prevent a lot of wasted effort and much frustration.
USING A STATA SETUP FILE TO IMPORT ASCII DATA INTO STATA
This ICPSR webpage gives thorough instructions for using Stata setup files:
https://www.icpsr.umich.edu/cocoon/ICPSR/FAQ/0127.xml
Basically, the *.do file must be edited with a text editor, e.g., Notepad. The paths and filenames of the ASCII data
file (*.txt), the Stata dictionary file (*.dct) and the output file, i.e., the Stata dataset have to be specified with
COMPLETE filenames, including extensions. If there is an embedded space in the filename, it must be enclosed
in double quotes. Here is an example for ICPSR Study # 07644.
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This is the section that
needs to be edited. The
program will use the
filenames supplied in
the sections below.
Here is the edited version:
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The code to replace missing values is usually commented out, so it isn’t executed. To change missing values
to a “.” which Stata recognizes as a missing value, delete the comment markers ( /* and */) at the beginning
and end of the section.
Deleting these
will change all
missing values
to “.”
Finally, it is good practice to type the . exit command at the bottom of the file.
Save and close your edited version of the *.do file. Opening the file will launch Stata, and if all goes well, the
Stata dataset will be created!
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