WORKSHOP ON ECONOMIC ANALYSIS OF CLIMATE CHANGE

PRACTICAL LESSONS ON STATA 11

1

INTERACTIVE USE OF STATA• Interactive use means that STATA commands are

initiated within STATA.• A graphical user interface (GUI) for stat is

available. It enables almost all the STATA commands to be accessed using drop down menus.

• STATA allows users to directly type commands to execute a particular task.

• The standard procedure however in STATA is to aggregate the various commands needed into one file called a do-file that can be run with or without interactive use.

BASICS IN STATA• Like most softwares, STATA has some example

data sets that allows ‘amateur’ users to use as starting point in learning STATA.– An example of such data sets is the auto.dta data

• To access the example data:– Click File/Example Datasets/… Example datasets

installed with Stata• Select the data set auto.dta

– Interactive Users can however type the command• sysuse auto

• To describe the variables in the data set type:– describe or des– Or to describe some specific variables type add the name of the

variable to the command.• Eg: des mpg• NB: stata commands does not allow upper case

• If you wish to the summary statistics of the variable type:• summarize,detail• sum, detail• su, detail• su, d

– You can drop the subcommand detail if you wish to obtain the basic summary statistics.

– You can summarize specific variables• sum varlist, detail • Eg: sum mpg, detail

– sum mpg– su mpg

DATA MANAGEMENT

DATA MANAGEMENT

• If you are only interested in a subset of your data, you can inspect it using filters. E.g. If you are only interested in price of a particular type of car you can type:– sum if price>=3000 & price<=4400– sum if mpg>=16& mpg<=23

• And then you can contrast – sum if price>=3000 |price<=4400– sum if mpg>=16 |mpg<=23

• Interpretation of Logical Operators in STATA.>= greater or equal to<= less or equal to== equal to& and| or

!= or ~= not equal to> greater than

< Less than. missing

DATA MANAGEMENT• The usual arithmetic operators (+,-,*,/) are

applicable in STATA.

• STATA allows users to tabulate variables to know the distribution of a variable– tabulate mpg– tab mpg

DATA MANAGEMENT• Some data/variables have been coded with value

labels already assigned to the values. If the user wants to know the actual values used type:– tab varlist, nolabel– Eg: tab foreign, no label

GENERATING NEW VARIABLES• You can create a new variable by combining new

variables or by performing some arithmetic operations. [gen, egen, recode]

• To create a ratio of two variables:– gen mpgratio=mpg/weight – sum mpgratio

The same procedure can be applied to obtain traditional transformations such as:

Square gen mpg2=mpg^2Cubic gen mpg3=mpg^3Square roots gen mpgsqrt=sqrt(mpg)Exponential gen expmpg=exp(mpg)Natual logs gen lnmpg=ln(mpg)

gen logmpg=log(mpg)Base 10 genl10mpg=log10(mpg)

DATA MANAGEMENT

• Eg: gen lprice=log(price+1)– Why +1? This helps eliminate the problem of

estimating the log of zero or missing numbers.• Sometimes the user may want to generate a new

variable within a particular range.– gen lprice=log(price) if mpg==.– gen llprice=log(price) if mpg>15

• The generate command can also be used to create new (binary) variables.– Eg: from the auto.dta data set we are using, may be

interested in finding out how many cars were repaired more than two times in 1978. Thus we create a new variable repair =1 if the vehicle was repaired more than twice or 0 if otherwise.

DATA MANAGEMENT

• Use the command:gen repair =1 if rep78>2replace repair=0 if rep78<=2 or replace repair=0 if repair==.

• You can also create categorical variables from a set of continuous variables.tab mpggen mpgcat=1 if mpg<15replace mpgcat=2 if mpg>=16& mpg<26replace mpgcat=3 if mpg>26 & mpg<=35replace mpgcat=4 if mpg>35tab mpgcat

DATA MANAGEMENT

• tabulate….., generateThis command is useful for creating a set of

dummy variables (variables with a value of 0 or 1) depending on the value of an existing categorical variable. The syntax is:tab old var, gen (new var) Eg: tab rep78, gen(repair)

tab foreign, gen(origin)• The old variable is categorical. The new

variables will take the form: newvar1, newvar2, newvar3…….

DATA MANAGEMENT

EGENThis is an extended version of “generate” to create a new variable by

aggregating the existing data. The syntax is: egen newvar = fcn(argument) [if exp] [in range] , by(var)

where newvar is the new variable to be created fcn is one of numerous functions such as: count( ) ; max( ); min( ) ; mean( ); median( ); rank( ) ; sd( ); sum( ); argument is normally just a variable var in the by() subcommand must be a

categorical variable.Eg: Egen avg=mean(mpg) : creates variable of average mpg over entire sampleEgen avg2=median (weight), by (foreign) : creates variable of median weight

of cars for each origin.egen totalrepairs=sum(rep78), by(foreign) : generates total repairs

of vehicles from each origin.egen prodwgt= sum(weight*price), by (make)

DATA MANAGEMENT

recode• This command changes the values of a

categorical variable according to the rules specified. The syntax is:– recode varname oldvalue=newvalue

oldvalue=newvalue … [if exp] [in range] – recode foreign 0=1 1=2– Recode rep78 .=9 *=7

DATA MANAGEMENT

recode is also an extension to replace that recodes categorical variables and generates a new variable if the generate () option is used.

recode rep78(1/2=1) (3=2) (4/5=3), gen (repcat)This creates a new variable that takes on value of 1,2 or 3. The repcat variables is set to missing if rep78 doesn’t lie in any of the ranges given in the recode command.

DATA MANAGEMENT

Xtile• This command creates a new variable that

indicates which category a record falls into, when the sample is sorted by an existing variable and divided into n groups of equal size.

• The syntax is:– xtile newvar=variable[if exp][in range],nq(#)Newvar is the new categorical variable created. Variable

is the existing variable used to create the quantile. # is the number of different categories.

Eg: pctile mpg1quint= mpg, nq(5)pctile weight1dec=weight, nq(5)

LIST The most detailed of the commonly used descriptive commands is list.

List displays the values of variables by observation. If varlist is not specified the output will contain the value for every variable.

list varlist ,or l varlist Eg: list mpg

Xi: Indicator VariablesA complete set of mutually exclusive categorical indicator dummy

variables can be created in several ways. A simpler method is the xi command:

xi i.rep78, noomitThe noomit option is added because the default setting is to omit the

lowest category.INSPECTinspect variable [if exp] [in range] Gives a small histogram, the number of values that are: unique; positive,

zero, negative; integer and non-integer; missing.

LABEL VARIABLE This command is used to attach labels to variables in order to make the output easier to

understand. For example, we know that maritalstat indicates the marital status of the head of household. But other people using the tables may not know this. So we may want to label the variables as follows:

label variable region “Region of country” Label variable maritalstat “marital status”

LABEL VALUES This command attaches named set of value labels to a categorical variable. The syntax is:

label values varname lblname where varname is the categorical variable which will get the labels lblname is a set of labels that have already been defined by label define

Here are some examples of labeling values in Stata. label variable yield "Yield (tons/hectare)" gives label to variable yield label define yesno 0 no 1 yes defines set of labels called yesno label values electricity yesno attaches labels to the variable “electricity” label define yesno 3 "perhaps", add adds new value label to existing set label define yesno 3 "maybe", modify modifies existing value label label define reglbl 1 West 2 Center 3 East defines regional labels label values region reglbl attaches regional labels to region label define reglbl 2 Central, modify modifies regional labels

TABULATE … SUMMARIZE • This command creates one- and two-way tables that summarize

continuous variables. The command tabulate by itself gives frequencies and percentages in each cell (cross-tabulations). With the “summarize” option, we can put means and other statistics of a continuous variable.

• The syntax is: tabulate varname1 varname2 [if exp] [in range], summarize(varname3)

options • where

– varname1 is a categorical row variable – varname2 is a categorical column variable (optional) – varname3 is the continuous variable summarized in each cell – options can be used to tell Stata which statistics you want

• tab make, sum(mpg) gives the mean, std deviation, and frequency of mpg for each car model.

• tab make, sum(price) mean gives the mean price for each car• tab foreign weight, sum(price)

Tabstat This command gives summary statistics for a set of continuous

variable for each value of a categorical variable. The syntax is: tabstat varlist [if exp] [in range] , stat(statname [...])

by(varname) where varlist is a list of continuous variables statname is a type of statistic varname is a categorical variable.Example:

table This command can creates many types of tables. It is probably the most

flexible and useful of all the table commands in Stata. The syntax is: table rowvar colvar [if exp] [in range], c(clist) [row col] where rowvar is the categorical row variable colvar is the categorical column variable clist is a list of statistic and variables row is an option to include a summary row col is an option to include a summary columnExamples:table foreign, c(mean rep78 sd rep78 median rep78) – table of yield

statistics by region . table foreign rep78, c(mean mpg) –table of average mpg by foreign

rep78• table foreign, c(mean rep78 mean mpg) –table of average rep78 &

mpg by foreign

MODIFYING DATA FILES • This section describes a number of commands that are used to

modify and combine data files in Stata.rename , drop , keep,

rename This command renames variables. Syntax:

rename oldname newname • Eg: rename mpg mile_per_gallondrop This command deletes records or variables. drop if price>=4000drop if foreign==1keep This command deletes everything but specified observations or

variables.Keep if price<=3000keep mpg rep78 headroom trunk if foreign

PRESENTING DATA WITH GRAPHS• In Stata, graphs are primarily made with the graph command, followed by

numerous subcommands for controlling the type and format of graph. In addition to graph, there are many other commands that draw graphs.

graph twoway

bar pie matrix

connect( ) msymbol( )

histogram scatter

http://www.stata.com/support/faqs/graphics/piechart.html

graph This command generates numerous types of graphs and

diagrams. The syntax is: graph graphtype [varlist] [if exp] [in range] [, options] where graphtype is the type of graph varlist is the list of variables to graph if is used to limit observations that are included based on the

exp condition in is used to limit observations that are included based on the

case number options are commands to control the look of the graph

PRESENTING DATA WITH GRAPHS

• graph bar income, over(sexhead) over( locality)

Histogramshistogram income, by(sexhead) normal bin(20)histogram income, by(locality) normal bin(20)histogram mpg, by( foreign) normal bin(20)Nb: bin () refers to the number of columns it

should include in the histogram

Scatter Plotsscatter mpg pricescatter mpg price,by(foreign)

• PIE CHARTSIn Stata, pie and bar charts are drawn using the sum of the variables

specified. Therefore, any zero values will not appear in the chart, as they sum to zero and make no difference to the sum of any other values. If you have a categorical variable that contains labeled integers (for example, 0 or 1, or 1 upwards), and you want a pie or bar chart, you presumably want to show counts or frequencies of those integer values. To create pie charts, first run the variable through tabulate to produce a set of indicator variables:

Eg:tab foreign, gen (f)graph pie f1 f2Try:tabulate rep78, generate(r) .graph r1 r2 r3 r4 r5, pie graph r1 r2 r3 r4 r5, bar

• Do-file Editor A Do-file is a file that stores a Stata program (a set of

commands) so that you can edit it and run it later.The Do-file Editor is like a simplified word processor for

writing Stata programs. Why use the Do-file Editor rather than the Command window or the menu system? – It makes it easier to check and fix errors, – it allows you to run the commands later, – it lets you show others how you got your result, and – it allows you to collaborate with others on the analysis. – In general, any time you are running more than 5-10

commands to get a result, it is easier and safer to use a Do-file to store the commands.

• LOG FILES• You can click on File/Log to begin or close a log file (Suspend

and Resume are to temporarily turn off and on the log). • You can use “log” commands in the Command window • You can use “log” commands in a Do-file.

OPENING FILES STATA FILES (.dta) To open a stata file: use filename, clear Eg: use "G:\fenergydata.dta", clearuse varlist using filename, clear [for a subset of the data file].Alternatively you can use the drop down menu bar to import the data

– File/open/………………….. (select the data)IMPORTING EXCEL DATA To import data from excel, one has to convert the data into an CSV [tab

delimited] format. For non stata files, the command for importing data is “insheet using”– insheet using filename, clear– Eg: insheet using "C:\Users\myjumens\Desktop\fenergydata.csv"

• Alternatively you can use the drop down menu bar to import the data.– File/import/ASCII data created by spreadsheet/ …… (select the data)

CODING QUESTIONAIRES INTO STATA

• Coding data into STATA can be done in the DATA VIEW– Generate new variables.

Eg: gen q1=. gen q2=.

– Click Data Editor on the menu bar– Click on Variable manager

Type the variable name

Type the variable label

Click on the manage to display a new dialog boxClick Apply to add your commands

into the system

• Creating Value Labels Click on create label

Type the value label here

Type in the value. Eg: 1

Type in the corresponding label to the values assigned

Click on Add

• Note that you can create all the value labels for all the questions before exiting the manage value label dialog box

• Assign the imputed value labels to their corresponding questions, or variables in the Variables Manager.

• Exit the Variables Manager dialog box and go back to the data editor.

• You can now type in the coded response.

MICROECONOMETRICREGRESSION ANALYSIS

• Ordinary Least Squares• Probit Models• Logit Models• Ordered Probit/Logit Models• Multinomial Logit Models• Tobit Models

Ordinary Least SquaresLike most statistical packages, STATA allows

users to run some basic regressions such as the OLS.

The syntax is:regress dependent var independent varEg: regress gpa tuce psi

reg gpa tuce psi

LOGIT AND PROBIT MODELS• Probit and logit models are among the most

widely used members of the family of generalized linear models in the case of binary dependent variables.

• These group of models allows researchers to analyse data on issues even though the dependent variables are binary (0, 1). – Eg: yes/ no; married or not married; foreign or

domestic

PROBIT MODELLet us examine whether a new method of teaching economics, PSI, significantly influence performance in later economics courses using the probit model. The dependent variable used is GRADE, which indicates whether a student’s grade in intermediate macroeconomics course was higher than that in the principle course.

The probit model is specified as:

• Estimation of Probit Model probit grade gpa psi tuce

• The basic probit commands report coefficient estimates and the underlying standard errors. These coefficients are the index coefficients and what we can only say is the direction of the effect and partial effects on the Probit index/score. They do not correspond to the average partial effects.

• Let’s try to interpret the results: – Tuce: one unit increase in tuce increases the probit

index by 0.05 standard deviations. – But are we concerned with an Probit index? No

• In analysing binary choice models the parameter of interest are not the index coefficients, rather the marginal/ partial effects.

Marginal Effects

• It gives the derivative of the probability that the dependent variable equals one with respect to a particular conditioning variable.

In stata these marginal effects can be computed using two methods – dprobit – mfx compute

Interpretation For one unit increase in the dependent variable

from the baseline, the probability of an event is expected to increase/decrease For instance one unit increase in GPA from the baseline (3.11), the probability of grade improvement increases by 53.3 %.

NB: The interpretation for dummy variables differs: The coefficients are discrete changes not marginal effects

The interpretation of PSI is that a student exposed to PSI has a probability of grade improvement of 0.46 greater than another student who is not exposed to the same method.

LOGIT MODELThe logit model yields similar results as the

probit model.

• The coefficients of the logit function is quite difficult to interpret since it follows a logistic distribution function.

• As a results we compute the odds ratio and the marginal effects

• MARGINAL EFFECTS• In stata these marginal effects can be

computed using the mfx command.

• Recall that for one unit increase in the dependent variable from the baseline, the probability of an event is expected to increase/decrease by the magnitude of the marginal change holding other variables constant

• In our case one unit increase in GPA from the baseline mark of 3.11 increases the probability of grade improvement by 53.3%

• One unit increase in the previous knowledge of the material from the baseline (21.93) increases the probability of grade improvement by 1.8 %.

• What about PSI?

ODDS RATIO• Odds are a way of presenting probabilities,

but unless you know much about betting you will probably need an explanation of how odds are calculated. The odds of an event happening is the probability that the event will happen divided by the probability that the event will not happen.

• Stata command: (or) ologit grade gpa psi tuce, or

Being exposed to new teaching methods (PSI) increases the odds of performing well by 0.79 .For every 1 unit increase in GPA, the odds of improving performance by a factor of 16.87

ROBUSTNESSCross sectional data are usually plaqued by the

problem of heteroscedasticity.• This statistical deficiency has implications on

the results of binary choice models.• Thus to report standard errors that are robust

we use the subcommand r or robust.– Eg: probit grade psi tuce gpa, r

probit grade psi tuce gpa, robust

ORDERED PROBIT/LOGITSome multinomial choice models are inherently ordered.

Examples include: • Bond ratings • Opinion surveys • Assignment of military personnel to job classifications by skill level • Voting outcomes on certain programs • The level of insurance coverage taken by a consumer: none part, or

full • Employment status: unemployed, part-time, or full

time

• In each of these outcomes, the outcome is discrete but the multinomial logit, conditional logit, nested logit models would fail to account for the ordinal nature of the dependent Variables.• The ordered probit/logit models however, accounts for

these ordinal properties.

ORDERED PROBITSuppose we wish to analyze the 1977 repair

records of 66 foreign and domestic cars. The 1977 repair records take on poor, fair, average and good and excellent. The main research problem is to explore the factors that explain the repair records in 1977.

The categories are;1. Poor 2. Fair 3. Average 4. Good 5. Excellent

MARGINAL EFFECTS• We need the marginal effects to interpret the

results of ordered probit effectively • The marginal effects show how the probabilities

of each outcome change with respect to changes in regressors.

• To calculate the marginal effects we run the mfx command separately for each outcome.– mfx, predict(outcome(1))– mfx, predict(outcome(2))– mfx, predict(outcome(3))– mfx, predict(outcome(4))

ORDERED LOGIT

MARGINAL EFFECTS

MULTINOMIAL LOGITThe multinomial logit (MNL) model, also known as multinomial logistic regression, is a regression model which generalizes logistic regression by allowing more than two discrete outcomes. That is, it is a model that is used to predict the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables (which may be real-valued, binary-valued, categorical-valued, etc.).

IMPLEMENTATION IN STATA• Stata uses the mprobit command to estimate the MNP. To

use mprobit we must have a single observation for each decision maker in the sample.

• Eg: We use data in on the type of health insurance available to 616 psychologically depressed subjects in the US. Patients may have either an indemnity (free-for-service) plan or a prepaid plan such as a Health Management Organisation-HMO) or the patient may be uninsured .

• Demographic variables include age, gender, race and site. • Indemnity insurance is the most popular alternative so

stata will choose it as the base outcome by default;

– The main research problem is to explore the factors that explain the choice of the health insurance

mprobit insure age male nonwhite site2 site3

Computation of the Marginal effects • We need the marginal effects to interpret the results of MNP

effectively. • The marginal effects show how the probabilities of each

outcome change with respect to changes in regressors • To calculate the marginal effects we run the mfx command

separately for each outcome.

Interpretation:

• TOBIT MODEL• There are instances where by the variable we are

investigating are censored at a point.• For instance our research objective is to explore

the factors that explain the repair records in 1977.

• Mpg in our data ranges from 12 to 41 • Assume that our data is censored so that we

could not observe a mileage rating below 17 mpg.

CENSORE THE MPG • If the true mpg is 17 or less, all we know is

that the mpg is less than or equal to 17. • Let’s first generate a new variable called mpg1 – gen mpg1=mpg

• Replace any value that is equal to 17 and below with 17 – replace mpg1=17 if mpg<=17– (14 real changes made)

Lets see what we actually observe after censoring

IMPLEMENTATION IN STATA • Notice that our dependent variable mpg is not

dichotomous but continuous. • Let’s run two regressions • Create wgt by dividing weight by 1000 to

make our discussions interesting • gen wgt=weight/1000

TYPES OF TOBIT• Left censored Tobit model • Right censored Tobit modelWe can estimate a tobit model by instructing

the software to censore the data both from below (left censore), above (right censored) or both.

Left censored Tobit model– Using the already censored data mpg1

• Using the uncensored data, we could instruct the software to censore it in the estimation by using the subcommand: , ll(…)– tobit mpg wgt, ll(17)

Right censored Tobit model

Two-limit Tobit models

• Tobit regression coefficients are interpreted in the same manner as ols regression coefficients.

• For a one unit increase in WEIGHT, there is a 6.2 point decrease in the predicted value of mpg. In other words a unit increase in the weight of the car is associated with a 6.2 units decrease in millage.

Computation of the Marginal effects • We need the marginal effects to interpret the results of tobit

model effectively. • The marginal effects show how the probabilities of the

outcome change with respect to changes in regressors • To calculate the marginal effects we run the mfx command

• NB: The marginal effects are just the same as from the regression model

Starting with do files

version 11set mat size 400

clear set mem 1000

capture log closeset more off