School

University of Queensland
Department

Economics

Course Code

ECON2300

Professor

All

Study Guide

Final

Econometrics: ] – Lecture 1

The Econometric Model:

Econometrics is about how we can use theory and data from economics, business and the

social sciences, along with tools from statistics, to answer “how much” type questions.

In economics we express our ideas about relationships between economics variables using the

mathematical concept of a function. An example of this is when expressing the price of a

house in terms of its size.

Price = f(size)

Hedonic Model: A model that decomposes the item being researched into its

constituent characteristics, and obtains estimates of the contributory value of

each characteristic An example of a hedonic model for house price might be

expressed as:

Price f (size,bedrooms,bathrooms, stories, age, pool, airconditioning)

Economic theory does not claim to be able to predict the specific behaviour of any individual

or firm, but rather is describes the average or systematic behaviour of many individuals for

firms.

Economic models = Generalisation

In fact we realise that there will be a random and unpredictable component e that we will call

random error. Hence the econometric model for price would be

Price f (size,bedrooms,bathrooms, stories,age, pool,airconditioning) e

The random error e, accounts for the many factors that affect sales that we have omitted from

this simplistic model, and it also reflects the intrinsic uncertainty in economic activity.

Take for example the demand relation:

qd f (p, ps, pc,i) 1 2p 3ps 4pc 5i

The corresponding econometric model is:

qd f (p, ps, pc, i) 1 2p 3ps 4pc 5i e

Econometric Models include the error term, e

In every model there are two parts:

1. A systematic portion – part we obtain from economic theory, includes assumptions

about the functional form.

2. An unobservable random component – “noise” component which obscures our

understanding of the relationship among variables: e.

How Do we Obtain Data?

In an ideal world:

1. We would design an experiment to obtain economic observations or sample

information

2. Repeating the experiment N times would create a sample of N sample observations

In the real world:

Economists work in a complex world in which data on variables are “observed” and rarely

obtained from a controlled experiment. It is just not feasible to conduct an experiment to

obtain data. Thus we use non-experimental data generated by an uncontrolled experiment.

Experimental data: Variables can be fixed at specific values in repeated trials of the

experiment

Non-experimental data: Values are neither fixed nor repeatable

Most economic, financial or accounting data are collected for administrative rather than

research purposes, often by government agencies or industry. The data may be:

• Time-series form – data collected over discrete intervals of time (stock market index,

CPI,

GDP, interest rates, the annual price of wheat in Australia from 1880 to 2009)

• Cross-sectional form – data collected over sample units in a particular time period

(income in suburbs in Brisbane during 2009, or household census)

• Panel data form – data that follow individual microunits over time (data for 30

countries for the period 1980-2005, monthly value of 3 stock market indices over the

last 5 years)

Data may be collected at various level of aggregation:

• Micro – data collected on individual economic decision-making units units such as

individuals, households, or firms

• Macro – data resulting from a pooling or aggregating over individuals, households, or

firms at the local, state, or national levels

Data collected may also represent flow or a stock:

• Flow – outcome measures over a period of time, such as the consumption of petrol

during the last quarter of 2005

• Stock – outcome measured at a particular point in time, such as the quantity of crude

oil held by BHP in its Australian storage tanks on April 1, 2002, or the asset value of

Macquarie Bank on 5th July 2009.

Data collected may be quantitative or qualitative:

• Quantitative – numerical data, data that can be expressed as numbers or some

transformation of them such as real prices or per capital income

• Qualitative – outcomes that of an “either-or” situation that is whether an attribute is

present or not. Eg. Colour, or whether a consumer purchased a certain good or not

(Dummy variables)

Statistical Inference:

The aim of statistics is to “infer” or learn something about the real world by analysing a

sample of data. The ways which statistical inference are carried out include:

• Estimating economic parameters, such as elasticities

• Predicting economic outcomes, such as the enrolments in bachelor degree programs

in Australia for the next 5 years. Testing economic hypotheses, such as: Ii

newspaper advertising better than “email” advertising for increasing sales?

Econometrics includes all of these aspects of statistical inference. There are two types of

inference:

1. Deductive: go from a general case to a specific case: this is used in

mathematical proofs

2. Inferential: go from a specific case to a general case: this is used in statistics

Review of Statistic Concepts:

Random variables: Discrete and Continuous

Random variable: A random variable is a variable whose value is unknown until it is

observed, it is not perfectly predictable. The value of the random variable results from an

experiment (controlled or uncontrolled). Uppercase letters (e.g. X) are usually used to denote

random variables. Lower case letters (e.g. x) are usually used to denote values of random

variables.

Discrete random variable:

A discrete random variable can take only a finite number of values that can be counted by

using the positive integers

• E.g. The number of cars you own, your age in whole years, etc.

1 If person is female

• Dummy variables: D

Over 90% improved by at least one letter grade.

OneClass has been such a huge help in my studies at UofT especially since I am a transfer student. OneClass is the study buddy I never had before and definitely gives me the extra push to get from a B to an A!

Leah — University of Toronto

Balancing social life With academics can be difficult, that is why I'm so glad that OneClass is out there where I can find the top notes for all of my classes. Now I can be the all-star student I want to be.

Saarim — University of Michigan

As a college student living on a college budget, I love how easy it is to earn gift cards just by submitting my notes.

Jenna — University of Wisconsin

OneClass has allowed me to catch up with my most difficult course! #lifesaver

Anne — University of California

Join OneClass

Access over 10 million pages of study

documents for 1.3 million courses.

Sign up

Join to view

OR

By registering, I agree to the
Terms
and
Privacy Policies

Already have an account?
Log in

Just a few more details

So we can recommend you notes for your school.

Reset Password

Please enter below the email address you registered with and we will send you a link to reset your password.

Add your courses

Get notes from the top students in your class.