1. STATA Tutorial September 29, 2017
Jill Furzer Institute of Health Policy, Management, and Evaluation
Canadian Centre for Health Economics
September 29, 2017

2. Outline Why use STATA? Reading/Cleaning data Regression Analysis
Post-estimation Diagnostic Checks
Advanced Topics in STATA
STATA Resources

3. Why use STATA? Easy to work with
Interactive
Menu driven
Prior programming experience not required
(can be helpful)
Smooth learning curve

4. Software Learning Curves
Source:

5. Why use STATA? Easy to work with
Interactive
Menu driven
Prior programming experience not required
(can be helpful)
Smooth learning curve
Strong data set management tool

6. Review: Data Types
Cross-sectional: A collection of observations in one time period.
Micro-data, surveys of persons, countries, etc.
Time Series: Many points in time, but for one individual entity.
Usually in aggregated form, like rates or percentages over time
Panel: Cross-sectional + Time series data.
Survey of the same individuals over many years
STATA particularly useful for Panel Data. Wide range of features to handle the problems faced with this data.

7. Reading and Cleaning data

8. First Steps Case sensitive, so be careful: i.e.
regress y x
Success!
(if everything else is right)
Regress y x
error message
Step 0: Double-click on the Stata icon
Hint: commands show up as blue
Hint: errors show up as red

9. Variables Window Review Window Results Window Command Window
Can be arranged based on preferences
Command Window

10. Step 1: Start a Log File File  Log  Begin:
Stata will prompt you to name the file.
Pick a creative name (e.g. logfile1), then click ok
Stata will now record everything you do
Importing data, running commands, store regression output, etc.
Or by code: log using "/Users/jillfurzer/Documents/STATAtutoriallog.smcl”
Saves as a smcl file
Stata will now record everything you do (importing data, running commands, etc).
It will even store your regression output, which is handy to look at later

11. Step 2: Import Data File  Import  Choose appropriate option:
.csv (Comma Separated) is a common option
.xls (Microsoft Excel Format) and other formats are compatible too

12. Example: Importing a .xls File
Make sure “Import first row as variable names” is checked, then click ok

14. Step 3: Look at your Data
Type describe to obtain some useful information about your dataset:
String refers to data that has non-numeric characters (like saying male or female instead of 0/1)
Will need to destring data if it is to change it to numeric so that you can run calculations on it
To look at your data, type browse

15. Black text is for numeric variables
Blue text is labeled numeric variables
So Gender will correspond to 0/1 but labeled as male female for descriptive purposes
Red text is for character variables (called string variables in Stata)

16. Step 4: Clean Data Example: Convert Character variable to Numeric
Make use of Stata’s destring command:
destring [varlist] , {generate(newvarlist)|replace} [destring_options]
Eg: destring Age, replace ignore(NA)
So you replace the original string data with the destringed data and are ignoring any variable that has a NA

17. Step 4: Clean Data Example: Sorting the Observations and Variables
Sorting changes the order in which the observations appear. We can sort numbers, letters, etc.
- Example: sort x
Ordering changes the order variables in dataset appear.
- Example: order x y z
Ordering especially important if you are doing panel data or any kind of multivariate logit/probit

18. Step 4: Clean Data Example: Renaming existing variables
Command: rename
If you want to rename variable ‘ZGMFX10A’ as ‘height’
rename ZGMFX10A height

19. Step 4: Clean Data Command: label To label the dataset in memory:
Example: Labeling Data
Command: label
Gives descriptions to variables or data sets
To label the dataset in memory:
label data “National Population Health Survey”
To label a variable:
label var healthstat “Self-Reported Health Status”
To label different numeric values the variable may take:
label define vlhealthstat 1 Excellent 2 Very Good 3 Good 4 Fair 5 Poor
label values healthstat vlhealthstat
Define the value label however you like, common to just put vl infront of the value name
Or label define vlgender 1 Male 0 Female
If you make a mistake and want to drop a label it is the same procedure as dropping a variable but just put the work label in front to let STATA know that you are talking about the label and not the variable

20. Step 4: Clean Data Exploring Missing Values
Missing values are given by “.” in STATA
To count the number of missing values in a variable, use user-written command tabmiss
To install, type findit tabmiss in command window
To use, type tabmiss varname
Important Note: you can use “findit” to install other user written commands, as well as help files for commands in STATA
Use frequency tables
tab female year, m

21. Step 5: Basic Analysis Command: summarize
Example: Obtaining basic summary statistics
Command: summarize
Use to obtain basic summary statistics of 1 or more variables
mean, standard deviation, min, max, etc.
summarize [varlist] [if] [in] [weight] [, options]
sum weight height
Just saying summarize will list all of your variables
Summarize followed by specific variable names will give you specific stats
Correlate good for looking for relationships and also a good test before you start running regressions to see if there is going to be collinarity between variables (as ideally you want variables to provide different explanation power for the explanatory variable, having two highly correlated variables and skew results)

22. Step 5: Basic Analysis Example: Obtaining basic summary statistics
Command: correlate
Creates a matrix of correlation or covariance coefficients for 2 or more variables
correlate [varlist] [if] [in] [weight] [, correlate_options]
corr height weight

23. Step 5: Basic Analysis Example: Frequency tables command: tabulate
Calculates and displays frequencies for one or two variables
tabulate varname [if] [in] [weight] [, options]
tab KEYSEX
tab KEYSEX Age, r
Can tabulate on one variable or two
For two, options, using ,row will give you percentages

24. Step 5: Basic Analysis Example: More detailed descriptive statistics
Command: tabstat
tabstat varlist [if] [in] [weight] [, options]
This example calculates the sum of the variable
Default stat in tabstat is mean (no specification)
Other statistics: min, max, skewness, kurtosis...

25. Step 6: Variable Generation
Changing existing variables
Command: replace
changes the contents of an existing variable
Most useful in the following cases:
Creating binary and categorical variables
Fixing the missing values
Syntax:
replace oldvar = exp [if exp] [in range]
Ex: Replace responses coded as “no response” (-1 in this case) with missing values
replace variable = . if variable == -1

26. Step 6: Variable Generation
Creating a new variable
Command: generate
Syntax:
generate newvar = exp [if exp] [in range]
Example:
generate age_sq=age*age
Notes: Can type generate or gen for short

27. Step 6: Variable Generation
Create a Binary Variable (0 / 1)
Generate a variable equal to 0 for all observations
Replace it to be 1 for selected observations
Ex: Create a binary for people with income over $80,000:
gen highinc=0
replace highinc=1 if hh_inc>=80000

28. Step 7: Graphs and Plots Plain Text Plot
plot yvar1 [yvar2 [yvar3]] xvar [if exp] [in range] [, columns(#) encode hlines(#) lines(#) vlines(#) ]
ex: plot weight height
Graphics Plot (generates an image file)
[graph] twoway plot [if] [in] [, twoway_options]
ex. graph twoway scatter weight height

29. Graph Examples Two-way scatter plot twoway scatter yvar xvar
Two-way line plot
twoway line yvar xvar
Two-way scatter plot with linear prediction from regression of x on y
twoway (scatter yvar xvar) (lfit yvar xvar)
Two-way scatter plot with linear prediction from regression of x on y with 95% CI
twoway (scatter yvar xvar) (lfitci yvar xvar)

30. Saving data
If you’ve imported data into STATA from a spreadsheet, text file, etc., you may want to save it as a STATA dataset.
From STATA menu, go File  Save (will give you an option to replace the data if it already exists)

31. Regression Analysis

32. Fitting a Linear Model General notation for linear regression:
regress depvar [indepvars] [if] [in] [weight] [, options]
Where: Y is our dependent variable X is our independent variable(s)
Determining which variables are what is usually determined by theory
Research Question: Is there a relationship between weight and height?

33. Graphical Representation
Yhati – Estimated (or predicted) value of Y based on the regression coefficients
Yi – Actual Value of Y
ei – Residual (Difference between estimated Y and actual)
B1 – Constant term
B2 – Slope of line

34. Linear Model Output
Follows notation (reg Y X) β2 β1

35. Post Estimation

36. Post Estimation Obtaining residuals predict residuals, residuals
NB: The “residuals” after predict is just the name you want to give to the residuals. You can change this if you want to
Obtaining fitted values
predict fittedvalues, xb

37. Heteroscedasticity testing
OLS assumes homoskedasticity
We can test for this after running a regression
Option 1: Examine residual pattern from the residual plot
Plot the residuals vs. fitted values
rvfplot, yline(0)
Option 2: Formal test (Breusch-Pagan Test)
estat hettest

38. RVF Plot

39. Breusch-Pagan Test
Reject the null (no heteroskedasticity) in favour of the alternative (there is heteroskedasticity of some form).

40. Linearity testing OLS assumes a linearity
relationship between the Y and X’s is linear

42. Linearity testing OLS assumes a linearity
relationship between the Y and X’s is linear
To test for this after a regression:
Command: acprplot var, lowess
acprplot height, lowess

43. ACPRPLOT Stata

44. Testing for multicollinearity
OLS assumes independent variables (x’s) collinear
To test for this use correlation matrix
Command: correlate varlist (before regression)
Rule of thumb is >0.8
Drop variable/transform it (log income)

45. Testing for multicollinearity
Variance Inflation Factor
vif (after regression)
Rule of thumd VIF <5

46. Specification testing
Testing for omitted variables or model misspecification
RESET test
Syntax: estat ovtest
Reject at 0.01 level: model has omitted variables

47. Testing Normality of Residuals
OLS assumes errors are normally distributed
We can use the residuals to test this assumption.
Command:
predict r, residuals
kdensity r, normal

48. Parameter Hypothesis Testing
Test whether a parameter equal zero
i.e., Height has no impact on weight
testparm height or test (height)
Test both parameters equal zero
i.e., both height and age have no impact on weight
test (height weight)
Test if coefficients on two variables are equal
i.e., the effect of height on weight is the same as the effect of age on weight
test (height= weight)
Simple t-tests

49. Storing Estimation Results
To store results of regression
estimates store name
This is useful to analyze regression results after running multiple models, comparing models
To list multiple results side-by-side
estimates table name1 name2…name5, etc.
To export results to excel, word, or LaTeX
Use user-written command esttab:

50. Advanced Topics

51. Non-continuous outcome variables
Binary outcomes: probit or logit
(help probit; help probit postestimation)
Ordered discrete outcomes: oprobit
(help oprobit; help oprobit postestimation)
Categorical outcomes: mlogit
(help mlogit; help mlogit postestimation)

52. Panel Data Econometrics
Pooled Linear Regression
regress depvar [indepvars] [if] [in] [weight] [, options]
reg weight height age
Random Effects
xtreg depvar [indepvars] [if] [in] [, re RE_options]
xtreg weight height age year, re
Fixed Effects
xtreg depvar [indepvars] [if] [in] [weight] , fe [FE_options]
xtreg weight height age year, fe
Pooled linear regressions are used when you can’t specify what point in time that an observation takes place so you pool them all together as if they were cross section (no time trends)
Random effects assigns parameters to the effect that the underlying common characteristics play on the dependent variable
Fixed effects is a way to control for endogenity by assuming that there is a underlying relationship stemming from certain unifying characteristics (country effects, province effects, sibling effects, individual effects)
doesn’t make an assumption on the underlying distribution where as random effects does (FE is thus semi non-paramentic)

53. Standard Errors Heteroskedastic Robust Standard Errors
reg weight height age year, vce(robust)
Cluster Robust Standard Errors
reg weight height age year, vce(cluster id)
Bootstrapped Clustered Standard Errors
reg weight height age year, vce(bootstrap, cluster(id))

54. Working With Do-Files Motivation Why bother?
We can ovoid tediously running the same set of commands over and over again…
Creates a document listing all the commands we’ve run in plain text form
Increases our productivity with STATA!

55. How to get to do file editor:
File  New  Do-file
Or “Do-file Editor” button at top (depending on which version of STATA you have)

56. Inputs commands here
Press to execute

57. Careful with backslahes, they will only be ignored if you are running the whole file versus just individual lines of code

58. STATA Resources

59. STATA Online Resources
STATA manuals are freely downloadable from the above site
Typing help [topic] in the command window is also useful, but the online manuals generally contain more detail/examples

60. STATA Online Resources
UCLA Institute for Digital Research and Education
List of topics and STATA resources can be found here:

61. Other STATA Resources
Jones, A.M., Rice, N., d’Uva, T.B., Balia, S Applied Health Economics - Second Edition, Routledge Advanced Texts in Economics and Finance. Taylor & Francis
Cameron, A.C., Trivedi, P.K Microeconometrics Using Stata – Revised Edition, Stata Press books.
Allison, P.D Fixed Effects Regression Models, Quantitative Applications in the Social Sciences. SAGE Publications.

62. Useful Data Sites
Ontario Data Documentation, Extraction Service and Infrastructure (ODESI) website:
Computing in the Humanities and Social Sciences (CHASS) at U of T

63. Thanks for Listening Good luck with STATA!