1 Research Methods Lecture 2 The dummies’ guide to STATA Wiji Arulampalam 18/10/2006

1

Research Methods

Lecture 2

The dummies’ guide to STATA

Wiji Arulampalam18/10/2006

2

Econometrics Software

• You can use any software that does what you need

• See Timberlake for details of what does what well [www.timberlake.co.uk]

• PC Give is hard to beat for time series analysis– Microfit, EViews are good alternatives

• STATA does (just about) everything. • STATA (and everything else) is available as a

delivered application on the network.

3

WHY STATA

• Need to know how to use STATA for (i) Econometrics A [next term](ii) Econometrics B [this term](iii) Panel Data Econometrics [next term]

• E-Views demo will be given by the Econometrics tutors!

• The above two should be sufficient

4

STATA

• Hopefully you will have access by next week

• So full demo next week

• Stata command file wages.do and data file wages.dta on the module web page for you to practice

5

STATA

• Use STATA: FOR– large survey datasets (merging them) – complex nonlinear models (e.g. LDV’s)

• But see also LimDep– nonparametric and evaluation methods– you want to

• continue studying economics • be a professional economist • learn something new

– you hate PC Give.

6

Some useful websites

• Stata’s own resources for learning STATA– Stata website, Stata journal, Stata library, Statalist

archive– http://www.stata.com/links/resources1.html

• Michigan’s web-based guide to STATA (for SA)• UCLA resources to help you learn and use

STATA: – http://www. ats.ucla.edu/stat/stata – including movies and “web-books”

7

Accessing STATA

Available from your ‘Delivered Applications’

Double click on icon!

Wstata.exe

8

Buttons/Menu

9

Enter commands here

10

OR use the do editor to create a .do file

11

Results window

Better to save the output – more later

12

Click for Extensive Help OR

Type help in command line

help

13

Type help in command line

help xxx

14Exit, clear

15

Click and point in v9

Exit, clear Menu/tabs

16

Important features (1)

• NOTE– Always use lowercase in STATA– Otherwise you can get very confused

• More--more-- in your output window more output to come. [Press spacebar and the next page appears]– Command set more off turn this off

• Not enough memory [so reset!]– . set mem XXXm (allocate XXX mb of data)– . set matsize XXX (max matrix size XXX square)

17

Important features (2)

• To Break– To stop anything hit the “break” (menu button with red

cross, or hit Ctrl and C simultaneously)

18

Using data on disk (1)

• Opening a dataset

– datasets need to be rectangular

[variables in columns; observations in rows ]

– Stata datasets have a .dta extension

– Will read excel or text files

– Otherwise use Stat/Transfer to convert other format

files to stata files

19

Using data on disk (2)

• There are several ways of getting data into STATA: eg: wages.dta

. use wages (or click: file/open on the menu bar)

. use lwage ed exp in 1/1000 if fem==1

. insheet using wages.csv (or .txt)

(imports an Excel csv file or a “text” file)

20

Opens the file

List of variables

21

Basic data reporting (1)

• .describe (or press F3 key)

– Lists the variable names and labels

• .describe using wages

– Lists the variable names etc WITHOUT loading the

data into memory (useful if the data is too big to fit)

• .codebook

– Tells you about the means, labels, missing values etc

22

23

Basic data reporting (2)

• sort and count

– .sort personid

• sorts data by personid

– .count if personid==personid[_n-1]

• counts how many unique separate personids

• _n-1 is the previous observation

24

25

26

First look at the data (1)

• .list lwage ed exp in 1/10 if fem>=0

– Lists the first 10 rows of var1 to var3 for which var4≥0

• .tab fem union (or tabulate)

[variables should be integers]

– gives a crosstab of fem vs union

27

28

First look at the data (2)

• .summ fem union (or summarize or sum)– means, std devs etc for x1 and x2

• .corr ed exp in 1/100 if fem<1 (,cov)– correlation coeffs (or covariances) for selected data– .pwcorr ed exp lwage [does all pairwise corr coeffs]

29

30

31

32

Tabulating (1)

• tab x1 x2 if x4==0, sum(x3)

– gives the means of x3 for each cell of the x1 vs x2 crosstabulation for observations where x4=0

• tab x1 x2, missing

– Includes the missing values

• tab x1 x2, nolabel

– Uses numeric codes instead of labels

– Eg “1” instead of “NorthWest” etc

33

Tabulating (1)

• tab x1 x2, col– Gives % of column instead of count– Can get row percentages by using row instead– Or both by using row col

• table educ ethnic, c(mean wage) row col

– Customises the table so it includes the mean (or median or mx or count or sd ….) of wage by cells

34

Labelling

• Always have your data comprehensively labelled.label data “This is pooled GHS 90-99”

.label variable reg “region”

.lab define reglab 0 “North” 1 “South” 2 “Middle”

.lab values region reglab

• Tedious to do for lots of variables– but then your output will be intelligibly labelled– other people will be able to understand it in future

35

Data manipulation (1)

• Data can be renamed, recoded, and transformed:Command .generate or gen for short

. gen logrw=log((earn/hours)/rpi)

. gen agesq=age^2 (squares)

. gen region1=(region==1) (1 if true, 0 if not)

. gen ylagged=y[ _n-1 ]

(_n is the obs # in STATA)

36

Data manipulation (2)

• Command recode:

. recode x1 .=0, 1/5=1 (. is missing value (mv))

. replace rate=rate/100

. replace age=25 if age==250

. egen meaninc=mean(income), by (region)

(see help egen for details)

37

38

Data selection (1)

• You can also organise your data set with various commands:

. keep if _n<=1000 ( _n is the observation number)

. drop region

. drop if ethnic~=1

keeps only the first 1000 observations, drops region, and drops all the observations where the variable ethnic≠1 (~= is “not equal to”)

39

Data selection (2)

• Then save the smaller file for subsequent analysis

. save newfile

. save, replace (take care – it overwrites existing file)

40

41

Functions

• Lots of functions are possible. • See . help functions

– Obvious ones like• Log(), abs(), int(), round(), sqrt(), min(), max(), sum()

– And many very specialised ones.– Statistical functions

• distributions– String functions

• Converting strings to numbers and vice versa– Date functions

• Converting dates to numbers and vice versa– And lots more

42

Command files

• Stata command files have a .do extension• It is ALWAYS good practice to use a .do file

– you will know exactly what you have done. – It makes it easy to develop ideas.– And correct mistakes.

• . do wages.do, nostop– (echoes to screen, and keeps going after error

encountered)

• Or . run wages.do (executes “silently”)

43

Keeping track of output (1)

• Can scroll back your screen (upto a point)

• But better to open a log file at the beginning of your session, and close it at the end.

• Click on file, log, begin . Or type

. log using myoutput

. Commands……………………

. log close

[log command allows the replace and append options.]

44

Keeping track of output (2)

• Default is .smcl file extension (that STATA can read)

• .log extension gives an ASCII file that anything can edit

• ALWAYS LOG your output

is a good way of developing a .do file – since it saves the commands as well as the output

45

Next Lecture

Monday 23rd October F107 11:00-12:00

STATA demo