125.785 Research Methods in
Finance
Seminar One
Monday 17 July
1
Honest politicians make the other
95% look bad
-- Mark Twain
2
Overview

Administrative Issues
–
–
–
–


Aims and Objectives
Introduction
–

3
Timetable
Labs
Textbook
Assessment
Eviews
Readings: Chapters 1-3, Chapter 16 optional
Administration

The general format will be for 1st half
–
–
–
A 2 hour seminar
A 2 hour lab in either CLQB4 or IIMS5/6
Finish approximately 7pm.
Textbook is Studenmund
Using Econometrics: A Practical Guide. 5th Ed.

–
4
4th Edition can also be used.
Assessment




1 Assignment Due September 1: 20%
Quiz 1: 31 July (10%)
Quiz 2: 14 August (10%)
Quiz 3: To Be Advised (10%)
–
5
Probably 28 August
Web Support

Web CT should be available for students
In the interim, the following website also will
have material:

http://www.massey.ac.nz/~bjmoyle/mu/teach.html

6
Computer Labs



Your user-name is your student ID
Your password is your 4 digit pin number
You will benefit from bringing
–
–

7
A floppy disk OR
A USB drive (preferred)
We will use Eviews for this section of the
course
Learning Objectives

Develop your skills at estimating economic
relationships.
–
–

8
This skill cannot be memorised from a textbook or
lectures
The textbook and seminars are to assist and
guide you.
Increase your familiarity with statistical
software.
Learning Outcome

To give you a sufficient background that you
can:
–
–

9
Attempt a research project with some of the skills
you have learned; or
Can progress on to advanced techniques used in
financial econometrics without difficulty.
It is impossible to teach you all the tools you
might use in the constraints of this paper.
The Unreliability of Textbooks

This is an applied paper, not a theory paper.
–
–
–
–
10
Every data set you model, will have ‘different’
problems present.
It is impossible to memorise all the permutations
of problems that you will encounter.
Skilled researchers are those with good problemsolving strategies, not recall of textbook stylised
facts.
Most of this skill must be developed with practical
work.
Introduction to Research

A research project involves three stages
–
–
–
11
Choosing a Topic
Analysis
Writing Report
Choosing a Topic



Ideally choose something you are interested
in for motivation
Make sure you can get enough data
Make sure there is some substance to topic
–
–
Not purely descriptive
Not tautological (so obvious to be uninteresting).

12
E.g. does an increase in the number of bidders raise
prices?
Analysis

Develop your theoretical model first
–

Specify the model
–

13
What causes what?
Hypothesise the effects you expect
–

May involve reading literature
This must be done before you run any models
Collect the data
Analysis 2

Estimate the Equation
–

Document the results
–
14
This should take the least amount of effort
There must be enough information given, that
someone else could replicate your results.
Report Writing

This is an important step
–
–

15
The purpose of research is often to generate
information for a decision-maker.
Hopefully, a manager or policy-maker could read
your report and learn something new.
A box of computer printouts, neither informs
nor impresses.
Report Writing 2

A report should not gloss over or ignore
results that you did not expect.
–

Keeping a research journal can assist
–
16
It is a common mistake to not discuss results that
contradict your prior beliefs.
Record your hypotheses, regression results,
statistical tests etc.
Practical Advice

Use common sense and economic theory
–

Ask the right questions
–

Sometimes regression problems are a
consequence of the wrong specification
Know the context
–
17
E.g. Real variables should not be matched with
nominal.
Understand the problem, not just the statistics
Practical Advice 2

Inspect the Data
–

Keep it sensibly simple
–
–

Complexity is not ‘good’ for its own sake
Consider Occam’s Razor.
Look long and hard at your results
–
18
Graphs or summary statistics can reveal missing
variables, outliers or other anomalies.
Does it make sense? You have to explain this to
others.
Practical Guide 3

Practice data-mining with care
–

Be prepared to compromise
–
–
19
Exhaustive experimentation to ‘get’ the ‘right’
results shows you’re biased…
Trying to find the perfect model will drive you
crazy.
Real data tends to throw up intractable problems.
Practical Guide 4

Do not confuse statistical significance with
meaningful magnitude.
–
–

Report a sensitivity analysis
–
20
Trivial variables may have very small effects, but
are highly significant.
It is tempting to use statistical significance as a
measure of a model’s performance.
Do results vary of you change the sample period
etc?
Basic Stats

We use statistical tools
in this paper
–


–
21
But it is not a course in
statistics
We will estimate the
value of many
parameters
E.g.
–

A mean (average)
A regression coefficient

We signal our
uncertainty about the
parameter with a type
of ‘spread’.
E.g.
–
–

Variance
Standard Deviation
These uncertainty
measures form the
basis of statistical tests.
Recap



The main difference between statistics and
other maths, is answers will have 2
dimensions
In normal algebra, variables combine to
produce an explicit solution.
In statistics, we think in 2 dimensions
–
–
22
What we think the value of something is
How confident we are in that estimate
Correlation



Quantifies the
relationship between 2
variables.
-1 ≤ r ≤ 1
Correlations imply
–
–
23
Relationships or
associations
General tendencies


Correlations do not
prove causality
Correlations can be
shown graphically
Correlation between GDP and G
25000
CGDP
20000
CGDP
15000
10000
5000
0
0
24
10
20
30
40
CG
50
60
70
Regression

Suppose we wanted to
–
–

25
Forecast prices for an
asset.
Determine causes of
unemployment
A regression “models”
finance or economic
data

Regression Models can
be used for several
purposes.
–
–
–
Forecasting
Testing hypotheses
Detecting influential
variables
Simple OLS Model


26
We begin with the
Ordinary Least
Squares (OLS)
regression model
This generates a
‘straight line’ between 2
variables.


The line ‘approximates’
the relationship
between the two
variables
The variables are
–
–
Dependent (Y)
Independent or
explanatory (X).
Regression Example




Y is GDP per capita
X is Govt spending
We ‘explain’ Y in terms
of X
We can estimate Y if
we know
–
–
27
Intercept of line- constant
Slope of line
Note on Regression


Researchers can (potentially) use many
different regression techniques.
OLS is a convenient starting point.
–
–
28
But not all regression models use least-squares
methods.
If certain assumptions are met, OLS is the best
method to use.
Intuition

OLS is based on Cartesian Geometry.
–
–
–
29
The line we estimate (with intercept and slope),
comes closer to all the observations than any
other line.
We minimise the (sum of) distance between the
line and the observations (squared). This is an
idea that draws on geometry.
As a minimisation problem, it can be readily
solved with differential calculus.
Eviews

Eviews is a popular (and powerful)
econometrics program.
–
30
It is the software most students use for their
graduate research reports
Where menu to estimate
equations is located
31
Equation Estimation Menu
32
Dependent
Variable
Explanatory
Variable
Constant
33
Output
34
Readings

Studenmund
–
–

WebCT
–
35
Chapter 16 Statistical Principles
Chapter 1-3
Guide to Eviews- Introduction