Econ 444: Elementary Econometrics

The Ohio State University

Winter 2011

Jason R. Blevins

Lecture 5: Gretl Tutorial

1. Introduction

Gretl, the GNU Regression, Econometrics and Time-series Library, is an open source, cross-
platform package for econometric analysis. It is loosely comparable to other packages you might
be familiar with such as Eviews and Stata. It is available for download at the following website:

http://gretl.sourceforge.net/

.

2. A Simple Example

To introduce Gretl, we will create a data file using the one of the simple datasets we used in class

to calculate the OLS coefficients by hand. Then, we will use Gretl to import the dataset, plot
the data, calculate the regression coefficients, and plot the regression line. These tasks were
relatively easy to do by hand for small datasets with n = 3 observations and a single independent

variable, but for more realistic datasets using software such as Gretl is much more practical.

2.1. Creating a Plain-Text Data File

Recall our simple example dataset on stock price (Y

i

) and trade volume (X

i

):

X

i

Y

i

1

2

4

2

1

3

Open Notepad (any similar text editor will do) and create a new file consisting of a header

containing variable names (use “volume” and “price”) followed by one observation per line and

with whitespace (one or more spaces or a tab) between the X value and the Y value. The file

should look something like this (it’s easier to read if the columns line up):

volume

price

1

2

4

2

1

3

Save the file with a name that you can remember, something like

stocks.txt

and remember the

location of this file.

2.2. Loading the Data into Gretl

1. Open Gretl and select

File | Open data | Import | ASCII

.

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Econ 444: Elementary Econometrics

Lecture 5: Gretl Tutorial

2. Navigate to the file where you stored the simple dataset you just created and click

Open

.

3. Gretl will automatically parse the data file and provide some output, such as the following:

parsing /home/jblevins/projects/444/stocks.txt...
using delimiter ’ ’

longest line: 15 characters
first field: ’volume’
number of columns = 2
number of variables: 2
number of non-blank lines: 4

scanning for variable names...

line: volume price

scanning for row labels and data...
treating these as undated data

4. Gretl should then show the variables from the dataset in rows.

• The first row is

const

, which represents the “constant term” in the regression, corre-

sponding to the

β

0

coefficient.

• The second and third variables should be

volume

and

price

respectively.

5. When you load data from a plain text file like this, if you expect to continue working with it

in Gretl, you can always save the dataset in Gretl’s own format (with a

.gdt

extension).

2.3. Manipulating the Dataset

The Data menu contains many useful functions for viewing and manipulating datasets. You can

experiment with some of these features (but be careful not to actually modify the dataset yet):

•

Display values

•

Edit values

•

Print description

2.4. Understanding the Dataset

Gretl also has many tools for summarizing and plotting data.

1. Select a particular variable, such as

volume

, and try

View | Summary statistics

.

2. Select

View | Graph specified vars | X-Y scatter

. Choose

volume

as the X-axis variable and

price

as the Y-axis variable and click

OK

.

This graph isn’t great because the dataset only has three points which are on the axes and

overlap with the tick marks, but for datasets that are more rich, this tool can be more
useful.

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Econ 444: Elementary Econometrics

Lecture 5: Gretl Tutorial

2.5. Running a Regression

To calculate the OLS coefficients ˆ

β

0

and ˆ

β

1

for this dataset, select

Model | Ordinary Least Squares

.

Select

price

as the dependent variable and

const

(preselected) and

volume

as the independent

variables and click

OK

. You should see the following output:

Model 1: OLS estimates using the 3 observations 1-3
Dependent variable: price

VARIABLE

COEFFICIENT

STDERROR

T STAT

P-VALUE

const

2.66667

0.707107

3.771

0.16501

volume

-0.166667

0.288675

-0.577

0.66667

Gretl can also calculate:

• fitted values

• residuals

• fitted regression line

3. A More Realistic Example

Download and import the dataset

finaid.txt

from the course homepage at

http://jblevins.

org/courses/econ444w11/finaid.txt

. This dataset contains four variables related to finan-

cial aid at a liberal arts school (Occidental College):

finaid

amount of financial aid (dollars per year)

hsrank

GPA rank in high school (percentage)

male

indicator variable for male students

parent

expected family contribution (dollars per year)

Use Gretl to answer the following questions:

1. How many observations are in this dataset?

2. What is the mean and standard deviation of high school GPA rank?

3. Generate a scatter plot of financial aid (dependent variable) versus GPA (independent

variable). Does there appear to be a positive or negative correlation?

4. Consider the linear regression model

finaid

i

= β

0

+ β

1

hsrank

i

+ ε

i

What is the expected sign of

β

1

?

5. Now regress financial aid on a constant and high school GPA rank using OLS and write

down ˆ

β

0

and ˆ

β

1

.

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Econ 444: Elementary Econometrics

Lecture 5: Gretl Tutorial

6. Plot the fitted regression line. Does the slope coincide with the positive or negative correla-

tion you saw above?

7. Are these results at all surprising? Are there important omitted variables or other relevant

factors that we might not be controlling for?

8. Now, add an additional regressor,

parent

, to control for need (parental contribution). What

are the resulting coefficients?

9. How do we interpret the coefficients on

hsrank

and

parent

?

10. Plot the fitted regression of financial aid (Y-axis) on high school GPA rank (X-axis), con-

trolling for parental contribution (control variable). To do this, in the main Gretl window,
select

View | Graph specified vars | X-Y with control

. Does this relationship coincide better

with your previous expectations?

11. Finally, add the

male

indicator variable to the regression and write down the OLS coeffi-

cients ˆ

β

k

for k = 0,1,2,3.

12. What does the coefficient on

male

indicate?

4