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125.785 Research Methods in Finance

Seminar One

Monday 17 July

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Honest politicians make the other 95% look bad

-- Mark Twain

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Overview

Administrative Issues– Timetable– Labs– Textbook– Assessment

Aims and Objectives Introduction

– Eviews

Readings: Chapters 1-3, Chapter 16 optional

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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.– 4th Edition can also be used.

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Assessment

1 Assignment Due September 1: 20% Quiz 1: 31 July (10%) Quiz 2: 14 August (10%) Quiz 3: To Be Advised (10%)

– Probably 28 August

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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

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Computer Labs

Your user-name is your student ID Your password is your 4 digit pin number You will benefit from bringing

– A floppy disk OR– A USB drive (preferred)

We will use Eviews for this section of the course

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Learning Objectives

Develop your skills at estimating economic relationships.– 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.

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Learning Outcome

To give you a sufficient background that you can:– 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.

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The Unreliability of Textbooks

This is an applied paper, not a theory paper.– 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 problem-

solving strategies, not recall of textbook stylised facts.– Most of this skill must be developed with practical

work.

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Introduction to Research

A research project involves three stages– Choosing a Topic– Analysis– Writing Report

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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).

E.g. does an increase in the number of bidders raise prices?

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Analysis

Develop your theoretical model first– May involve reading literature

Specify the model– What causes what?

Hypothesise the effects you expect– This must be done before you run any models

Collect the data

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Analysis 2

Estimate the Equation– This should take the least amount of effort

Document the results– There must be enough information given, that

someone else could replicate your results.

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Report Writing

This is an important step– 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.

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Report Writing 2

A report should not gloss over or ignore results that you did not expect.– It is a common mistake to not discuss results that

contradict your prior beliefs.

Keeping a research journal can assist– Record your hypotheses, regression results,

statistical tests etc.

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Practical Advice

Use common sense and economic theory– E.g. Real variables should not be matched with

nominal.

Ask the right questions– Sometimes regression problems are a

consequence of the wrong specification

Know the context– Understand the problem, not just the statistics

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Practical Advice 2

Inspect the Data– Graphs or summary statistics can reveal missing

variables, outliers or other anomalies. Keep it sensibly simple

– Complexity is not ‘good’ for its own sake– Consider Occam’s Razor.

Look long and hard at your results– Does it make sense? You have to explain this to

others.

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Practical Guide 3

Practice data-mining with care– Exhaustive experimentation to ‘get’ the ‘right’

results shows you’re biased…

Be prepared to compromise– Trying to find the perfect model will drive you

crazy. – Real data tends to throw up intractable problems.

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Practical Guide 4

Do not confuse statistical significance with meaningful magnitude.– 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. Report a sensitivity analysis

– Do results vary of you change the sample period etc?

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Basic Stats

We use statistical tools in this paper

– 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.

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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– What we think the value of something is– How confident we are in that estimate

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Correlation

Quantifies the relationship between 2 variables.

-1 ≤ r ≤ 1 Correlations imply

– Relationships or associations

– General tendencies

Correlations do not prove causality

Correlations can be shown graphically

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Correlation between GDP and G

0

5000

10000

15000

20000

25000

0 10 20 30 40 50 60 70

CG

DP

CG

CGDP

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Regression

Suppose we wanted to– 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

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Simple OLS Model

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).

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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– Intercept of line-

constant– Slope of line

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Note on Regression

Researchers can (potentially) use many different regression techniques.

OLS is a convenient starting point.– But not all regression models use least-squares

methods.– If certain assumptions are met, OLS is the best

method to use.

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Intuition

OLS is based on Cartesian Geometry.– 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.

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Eviews

Eviews is a popular (and powerful) econometrics program.– It is the software most students use for their

graduate research reports

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Where menu to estimate equations is located

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Equation Estimation Menu

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Dependent Variable

Explanatory Variable

Constant

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Output

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Readings

Studenmund– Chapter 16 Statistical Principles– Chapter 1-3

WebCT– Guide to Eviews- Introduction