IRS Stata

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GETTING STARTED WITH STATA

Sébastien Fontenay

ECON - IRES

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THE SOFTWARE

Software developed in 1985 by StataCorp

Functionalities

› Data management

› Statistical analysis› Graphics

Using Stata at UCL

› Computer labs

Socrate 30, 31-32, 33, 34, 54 and 68

Dupriez 143

Leclercq 74, 76, 77 and 78

› Student licence to install on your personal computer

valid during all your studies at the price of 20 euros www.uclouvain.be/438229.html

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Best documentation

› help command

› search keyword

Stata website : www.stata.com/support

› Frequently Asked Questions› Video tutorials

› Statalist

Books

› Cahuzac, E., Bontemps, C. (2008). Stata par la pratique: Statistiques,

graphiques et éléments de programmation.

› Cameron, A.C., Trivedi, P.K. (2009). Microeconometrics using Stata.

› Becketti, S. (2013). Time series using Stata.

UCLA : www.ats.ucla.edu/stat/stata

FINDING SUPPORT (1)

http://www.stata.com/support

http://www.ats.ucla.edu/stat/stata

http://www.ats.ucla.edu/stat/stata

http://www.stata.com/support

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For all your questions related to data management or analysis using

Stata

› Website: http://www.uclouvain.be/411370

› Email: [email protected]

› By appointment only:

• Bâtiment Dupriez (office d010), 3 place Montesquieu

FINDING SUPPORT (2)

http://www.uclouvain.be/411370

mailto:[email protected]

mailto:[email protected]



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COURSE TOPICS

• Working environment

• Writing commands

Quick tour ofStata

• Inputting data

• Transforming data

Datamanagement

• Descriptive statistics

• Linear regression

• Exporting results

Data analysis

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SECTION 1



QUICK TOUR OF STATA

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The working environment is composed of 5 windows

WORKING ENVIRONMENT

› Results

of commands

› Review

• of commands

› Command

• window

› Variables

•

list and labels

› Properties

• of variables and

dataset

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Three specific windows can be opened by clicking on the following icons

› Data editor/browser

• Display data in memory

› Viewer• Display log and help files

› Do-file Editor

• Text editor to save/execute commands

There are 3 main types of files used in Stata› .dta data

› .do commands (do-file)

› .smcl | .log output (log file)

WORKING ENVIRONMENT

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Data Graphics Statistics

WORKING ENVIRONMENT

All software functionalities

are available from the drop-

down menus

› Useful when you are unsure

of commands to run orunfamiliar with available

options

Every command issued in

this manner is echoed to thereview and results windows

› e.g. sysuse auto.dta

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In order to use Stata effectively, you should always follow this

three-step process:

› Open a do-file

› Choose your working directory

• cd "C:\Users\Me"

• mkdir stata_training

• cd stata_training- You can see the current working directory at the bottom left of the main window

› Start a log file (saving commands and their output)• log using filename [, text append replace]

- log close

- log off | on

WORKING ENVIRONMENT

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SECTION 1



QUICK TOUR OF STATA

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Stata commands use a common syntax:

[ prefix :] command [varlist ] [= exp] [if] [in] [, options]

• The square brackets denote qualifiers that are optional

• Italicized words are to be substituted by the user

› varlist denotes a list of variables

› exp is a mathematical expression

Stata is case sensitive! (i.e. UPPERCASE != lowercase)

WRITING COMMANDS

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Arithmetic

+ addition- subtraction* multiplication / division^ raised to power

Relational

> greater than< less than

>= > or equal

<= < or equal

== equal

~= not equal

!= not equal

WRITING COMMANDS

Operators may be used to manipulate numerical or string variables

Logical

& and| or

! not

~ not

Pay attention that a double equal sign (==) is used for equalitytesting

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Logical and relational operators are particularly useful with if

qualifiers to define the sample for analysis

The if qualifier at the end of a command means the command is touse only the data specified

› command if exp

• list make if foreign==1

• list if make=="Volvo 260"

•

list make price if price>=5000 & price<=7000• list make price if price<5000 | price>7000

Note that character strings are enclosed in double quotes

WRITING COMMANDS

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1/30 1 to 30

1/l 1 until last number

f/-5 first to 5th number before the end

-5/l last five numbers

WRITING COMMANDS

You can refer to a list of numbers using the following shorthand

Numlists are particularly useful with the in qualifiers to specify a

range of observations to be used

› command in range• list in f/10

• list in -10/l

• list make price in 74

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The by prefix repeats execution of a command on subsets of the data

› subsets are groups of observations that take the same value in a given

variable (often a categorical variable)• by varname: command

- by foreign: list make

› If the dataset is not sorted, you should use the bysort prefix instead

•

bysort varname: command

WRITING COMMANDS

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SECTION 2

• Inputting data


DATA MANAGEMENT

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To open a dataset in Stata format (.dta): use

› use filename [, clear]

• sysuse - open example datasets installed with Stata

To save a dataset in Stata format: save

› save filename [, replace]

Stata can also import/export Excel files (.xls or .xlsx )

› import excel filename [, firstrow]

› export excel filename [, firstrow(variables)]

By default, Stata opens/saves a dataset from/in the current working

directory but you can specify

› another directory: use | save "C:\Users\Me\Stata_training\dataset.dta"

› a web address: use http://sites.uclouvain.be/datasupport/data/wage.dta

INPUTTING DATA

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Summary of the dataset

› describe: information on dataset in memory

› codebook: detailed description of variables

Further explore data in memory

› count: number of observations

› list: display data in the results window

Manipulate variables/observations

› keep wage educ exper

› drop in 1/10

› sort wage

INPUTTING DATA

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SECTION 2

• Inputting data


DATA MANAGEMENT

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To create a new variable: generate

› generate newvar = exp [if] [in]

• exp may be a number, a character string or a mathematical function

• generate constant = 1- Create a constant equal to 1

• generate constant_text = "text"- Create a constant that contains the character string "text"

• generate logwage = ln(wage)

- Create a variable equal to the natural logarithm of wage

• generate expersq = expr^2- Create a variable equal to the square of exper

TRANSFORMING DATA

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To create specific variables using time series operators

› generate lag_gdp = L.gdp

• Create a variable corresponding to the first lag of gdp

› generate lead_gdp = F.gdp

• Create a variable corresponding to the first lead of gdp

›generate diff_gdp = D.gdp

• Create a variable corresponding to the first difference of gdp

But before you should tell Stata that you are working with time series

data using the command: tsset

› tsset time [, yearly monthly quarterly daily]

Using system variables

› generate gdp_growth = ((gdp[_n] - gdp[_n-1]) / gdp[_n-1])*100

• Create a variable equal to the growth rate of gdp

TRANSFORMING DATA

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To modify an existing variable: replace

› replace wage=20 if wage>=20

To rename an existing variable: rename

› rename wage hourly_wage

You can also add a brief description to the variable using labels

›label variable educ "total years of education"

TRANSFORMING DATA

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When transforming data, one must be careful with missing values

› Missing values in Stata are coded with a . (period)

Stata treats missing values as large numbers, higher than any other

values of a given variable

› In certain cases you should use the if qualifier to exclude missing values

• generate rich = (wage>15) if wage<.

|or|

•

generate rich = (wage>15) if wage!=.|or|

• generate rich = (wage>15) if !missing(wage)

TRANSFORMING DATA

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SECTION 3




DATA ANALYSIS

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Categorical variables

› One-way table of frequencies

• tabulate female- The option [, missing] displays the total frequency of missing observations

› Two-way table of frequencies

•

tabulate female married

Continuous variables

› summarize gives the number of observations, the mean, the standard

deviation, the minimum and maximum values

• summarize wage educ

- The option [, detail] displays the main quantiles, the highest and lowest five values, the

variance, as well as the skewness and kurtosis measures

Pearson’s correlation coefficient

› correlate varlist [, covariance]

DESCRIPTIVE STATISTICS

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SECTION 3




DATA ANALYSIS

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LINEAR REGRESSION

We seek to estimate the relationship between one dependent variable

and a set of independent variables

› using the Ordinary Least Squares (OLS) estimator

Classical linear model assumptions (Wooldridge, 2008):

› Model is linear in parameters

› Data are random sample of the population

› No perfect collinearity between independent variables

› Zero conditional mean of error term› Homoskedasticity

› Normality of the residuals

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LINEAR REGRESSION

The model we want to estimate:

› log(wage) = 0 + 1education + 2experience + 3tenure + u

• where:

- wage is average hourly earnings in dollars

- education is the number of years of education

- experience is the number of years of labour market experience

- tenure is the number of years with the current employer

In Stata:› regress logwage educ exper tenure

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LINEAR REGRESSION

Stata output

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Analysis of variance

› Sum of Squares (SS)

•

Explained variance (model)• Residual variance

• Total variance

› Degrees of freedom (df)

› Mean Squares (MS)

• SS divided by df

LINEAR REGRESSION

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Parameters estimates

› Dependent variable (1)

› Independent variables and intercept (2)

› Coefficients (3)

› Standard-errors (4)

› t-statistics (5)› p-values associated with the t-statistics (6)

• testing the null hypothesis that a given coefficient is 0

› 95% confidence intervals (7)

LINEAR REGRESSION

(1) (3) (4) (5) (6) (7)

(2)

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LINEAR REGRESSION

Predicting fitted values and residuals› predict wage_fitted

• e.g. 1,304921 = 0,2843595 + 11*0,092029 + 2*0,0041211 + 0*0,0220672

› predict wage_resid, r

• e.g. -0,1735185 = 1,131402 – 1,304921

logwage educ exper tenure wage_fitted wage_resid

1 1,131402 11 2 0 1,304921 -0,1735185

2 1,175573 12 22 2 1,523506 -0,3479329

3 1,098612 11 2 0 1,304921 -0,2063083

4 1,791759 8 44 28 1,819802 -0,0280429

5 1,667707 12 7 2 1,461690 0,2060172

6 2,169054 16 9 8 1,970451 0,1986027

7 2,420368 18 15 7 2,157168 0,2631997

8 1,609438 12 5 3 1,475515 0,1339233

9 1,280934 12 26 4 1,584125 -0,3031912

10 2,900322 17 22 21 2,402928 0,4973939

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Incorporating categorical information into regression models

Dummy variables (coded as 0/1) can be included as such in the

regression› regress wage educ exper tenure female

Categorical variables with more than two categories must be included

using the i. prefix

› regress wage educ exper tenure i.region

• Stata will automatically create dummy variables for each category and

incorporate them in the regression except the reference category- You can use the prefix ib( x ). instead to change the reference category

LINEAR REGRESSION

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LINEAR REGRESSION

Post-estimation tests

› Multicollinearity (Wooldridge, 2008 - chapter 3, p99)• estat vif

- Rule of thumb, if variance inflation factor>10, multicollinearity problem

› Normality of the residuals

• sktest varname

- testing the null hypothesis that variable follows a standard normal distribution• swilk|sfrancia varname

- Shapiro-Wilk and Shapiro-Francia test

› Homoskedasticity (Wooldridge, 2008 - chapter 8)

• estat hettest- Breusch-Pagan test, testing the null hypothesis of homoskedasticity

• estat imtest, white- White test, testing the null hypothesis of homoskedasticity

• The [, robust] option after regress gives heteroskedasticity-robust standard errors

› F-test: testing that a group of variables has no effect on the dependent

variable – joint hypotheses test (Wooldridge, 2008 - chapter 4, p143)

• test var1 var2

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SECTION 3




DATA ANALYSIS

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EXPORTING RESULTS

outreg2 allows to easily export the results of one or several

regressions› to Microsoft Office applications: Word, Excel

› to LaTeX

outreg2 [estlist ] using filename [, word excel tex]

› [estlist ] refers to the list of estimation results previously saved using the

command: estimates store estname

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EXPORTING RESULTS

regress logwage educ

estimates store est1

regress logwage educ exper tenure


regress logwage educ exper tenure female


outreg2 [est1 est2 est3] using output, word

IRS Stata

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