Econ2271 ‐ Introductory Econometrics
Workshop
Economics, Business School
University of Western Australia
1
Workshop: Week One
Expectations
• All students watched the pre‐recorded lectures or
• At least went through the slides uploaded in LMS
• Bring your questions/queries today
• Being inter‐active
• Delivery mode in‐person and in class
2
1
Workshop: Week One
Content
• Highlights of lecture material
• In class quizzes
• Q&A
• Any other matters
3
Workshop: Week One
Lecture Outline: Week One
• General information about ECON2271
• What is econometrics?
• Steps in economic /econometric analysis
• Revision – several maths/stats concepts
4
2
Workshop: Week One
The focus of Econ2271
• Understanding
• Problem‐solving
• Application of concepts learned
• There is some technical treatment, but the focus is on
economic and business analysis
5
Course Assessment
Assessment structure
• Tutorials – 10%
• Once per week, starting in teaching week 3
• Must attend/participate in at least 8 tutes
• Attendance is recorded by the tutor
• Assignment #1 – 25%
• Assignment #2 – 25%
• Course project – 40% (in lieu of final exam)
6
3
Course Schedule
Weekly lecture/workshop (90 minutes)
1‐3pm, Tuesdays, [G89] Stan Perron AC
Lecture Theatre, Business School Building
(entry from outside/western end)
Blended teaching
7
Course Schedule
o Weekly tutorial (45 minutes), Trading Room
(room 124), Business School Building
o Start in teaching week 3
o Students are expected to go through the
tutorial questions in advance
o Students should attend their enrolled
sessions only (to avoid over-crowding the
Lab and ensure access to PCs/Eviews licence)
8
4
Course Schedule
o Office hours: Tuesdays 3.30-4.30pm, Room
112, Business School (or via appointment)
o Email: yanrui.wu@uwa.uwa.au
o Tel: 08 6488 3964
o Check your email account regularly
o Check LMS regularly
o Delivery mode: Blended
9
Workshop: Week One
Lecture Outline: Week One
• General information about ECON2271
• What is econometrics?
• Steps in economic /econometric analysis
• Revision
10
5
Workshop: Week One
Lecture Outline: Week One
• YW: Define econometrics in 1‐2 sentences
• ChatGPT: “Econometrics is a branch of
economics that uses statistical methods to
analyze and quantify economic phenomena. It
involves the application of mathematical models
and techniques to test economic theories and
inform economic policy”
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Workshop: Week One
Lecture Outline: Week One
• YW: Not quite correct
• ChatGPT: “I apologize for the mistake in my previous
response. Here's another attempt to define
econometrics in 1‐2 sentences: Econometrics is a
field of study that combines economics, statistics,
and mathematics to analyze economic phenomena
and test economic theories. It involves the use of
statistical methods to estimate and test models that
capture the relationships between economic
variables” .
12
6
Workshop: Week One
Lecture Outline: Week One
• YW: That is better
• ChatGPT: “Thank you, I'm glad I could provide a better
definition for you. If you have any other questions, feel
free to ask!” .
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Workshop: Week One
Lecture Outline: Week One
• Use of AI tool for coursework
• Statements from the University and School via LMS
• Econ2271: ChatGPT is a tool like Eviews which we
use
• We should make sure we use our tools correctly
• All assessments must be independently conducted
by students (not AI, not an agent)
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7
Workshop: Week One
Lecture Outline: Week One
• YW: Should I encourage students to use chatGPT for my
course
• ChatGPT: “As an AI language model, ChatGPT can provide
general information and guidance on a wide range of
topics, including those related to your course subject.
However, it is important to note that ChatGPT's responses
may not always be tailored to your specific course
material, and therefore should be used as a supplemental
resource rather than a primary source of information (bold
by YW).
15
Workshop: Week One
Lecture Outline: Week One
• ChatGPT: “Encouraging students to use ChatGPT can be
a helpful way to enhance their learning experience and
provide them with additional resources to support their
studies. However, it is also important to emphasize the
importance of critical thinking and independent
learning, and to encourage students to consult with you
or other qualified experts when necessary
• Overall, ChatGPT can be a valuable resource for
students, but it should be used in conjunction with
other learning materials and resources” .
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8
Workshop: Week One
Lecture Outline: Week One
• General information about ECON2271
• What is econometrics?
• Steps in economic /econometric analysis
• Revision
17
Workshop: Week One
Economic or econometric analysis steps:
1)Statement of theory or hypothesis
2)Specification of the mathematical model
3)Specification of the econometric model
4)Obtaining data
5)Estimation of the model
6)Hypothesis testing
7)Forecasting or prediction
8)Policy implication or analysis
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9
Workshop: Week One
4. Obtaining Data:
• In order to perform econometric analysis,
we need some data
• In general, three types of data
• Cross‐sectional
• Time series
• Pooled or panel data
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Workshop: Week One
An example : TS data
• Australian macroeconomic data
• Source ‐ Australian Bureau of Statistics or ABS
• GDP ‐ the income of the country
• Household Expenditure ‐ private consumption
• See how GDP and Household Expenditure
change over time?
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10
Household expenditure $billion
250
200
0
100
200
300
Household expenditure $billion
400
500
May‐2020
Jan‐2018
Sep‐2015
May‐2013
Jan‐2011
Sep‐2008
May‐2006
Jan‐2004
Sep‐2001
May‐1999
Jan‐1997
Sep‐1994
May‐1992
Jan‐1990
Sep‐1987
May‐1985
Jan‐1983
Sep‐1980
May‐1978
Jan‐1976
Sep‐1973
May‐1971
Jan‐1969
Sep‐1966
500
May‐1964
Jan‐1962
Sep‐1959
$Billion
Workshop: Week One
600
Trend curve or movement over time
400
300
200
100
0
GDP $billion
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Workshop: Week One
300
Scatter diagram or
scattergram
150
100
50
0
600
GDP $billion
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Workshop: Week One
Another example – CS data
• Life expectancy across nations (years)
• GDP per capita ($)
• Relationship b/w the two
• Source: World Bank
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Workshop: Week One
Life expectancy
90.0
85.0
80.0
75.0
70.0
65.0
60.0
55.0
$GDP pc
50.0
0
50000
100000
150000
200000
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12
Workshop: Week One
Log( life expectancy)
1.95
Log: “linearization” or
“smoothing”
1.90
1.85
1.80
1.75
Log(GDP pc)
1.70
0.00
1.00
2.00
3.00
4.00
5.00
6.00
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Workshop: Week One
Effective date
19/02/2024
20/02/2024
21/02/2024
22/02/2024
23/02/2024
26/02/2024
27/02/2024
28/02/2024
29/02/2024
1/03/2024
4/03/2024
5/03/2024
6/03/2024
7/03/2024
8/03/2024
11/03/2024
12/03/2024
13/03/2024
S&P/ASX 200
7665.1
7659.05
7608.36
7611.24
7643.59
7652.84
7663.01
7660.42
7698.7
7745.61
7735.79
7724.2
7733.54
7763.71
7846.97
7704.22
7712.53
7729.44
ts data
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13
Workshop: Week One
S&P/ASX 200
8800
8600
8400
8200
8000
7800
7600
7400
3/04/202
12/02/20
10/02/2025
20/01/2025
30/12/2024
9/12/2024
18/11/2024
28/10/2024
7/10/2024
16/09/2024
26/08/2024
5/08/2024
15/07/2024
24/06/2024
3/06/2024
13/05/2024
22/04/2024
1/04/2024
11/03/2024
19/02/2024
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Line chart – 1 year data
24/12/20
4/11/202
15/09/20
27/07/20
7/06/202
18/04/20
28/02/20
9/01/202
S&P/ASX 200
8800
27
Scattered chart
Workshop: Week One
8600
8400
8200
8000
7800
7600
7400
7200
7000
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Workshop: Week One
S&P/ASX 200
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
Line chart – 10 year data
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Quiz 01
1. What is the difference between a mathematic
and an econometric model?
2. “If the population is all UWA students, the
sample could be all Econ 2271 students. It
would, however, not be a good idea”. Why?
3. What is hypothesis test?
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15
Workshop: Week One
2. Specification of the mathematical model:
Y = β1 + β2X
o
o
o
(1)
An exact relationship
In this case, a linear relationship
β1 is the intercept; β2 is the slope
o If X increases by 1, Y should increase by β2
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Workshop: Week One
3. Specification of the econometric model:
Y = β1 + β2X + µ
o
o
o
o
(2)
Y: dependent variable
X: independent or explanatory variable
β1: intercept; β2 : slope coefficient
(β1 , β2) : parameters to be estimated
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16
Workshop: Week One
3. Specification of the econometric model:
Y = β1 + β2X + µ
o
o
o
(2)
What is µ? (others may use , u, v, …)
µ : disturbance or error term (random or
stochastic/non-deterministic variable)
Random variable: a variable whose outcome is
due to chance
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Workshop: Week One
3. Specification of the econometric model:
Y = β1 + β2X + µ
o
o
(2)
µ : Can capture other things that are changing,
perhaps in some year people have a change in
their tastes and spend more, or maybe prices
were extreme
Could also be a type of measurement error
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17
Quiz 01
1. What is the difference between a mathematic and an
econometric model?
A mathematical model is a theoretical construct that describes
relationships between variables using precise equations, often
deterministic in nature, while an econometric model
incorporates statistical methods to analyze real‐world economic
data, including uncertainty and random error terms, allowing
for empirical testing of economic theories through data
analysis; essentially, a mathematical model is a simplified
representation of a system without considering random noise,
while an econometric model explicitly includes this noise to
better reflect real‐world situations.
35
Quiz 01
1. What is the difference between a
mathematic and an econometric model?
2. “If the population is all UWA students, the
sample could be all Econ 2271 students. It
would, however, not be a good idea”. Why?
3. What is hypothesis test?
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18
Workshop: Week One
Sample vs population:
o Population: all possible observations
o Populations could be relatively small (students in this
class), large (all tertiary students in Australia), or even
infinite
o Sample: a subset of the observations in the
population
o Most of the time we only observe a sample of the
population rather than all outcomes
37
Workshop: Week One
Sample vs population:
o “Sample” examples- political surveys, census data, etc
o We use the information provided by the sample at hand
to estimate the population parameters
o Thus raise the question of sample
representativeness
o Econ2271 students representative of All UWA
students?
o All year-1 UWA students would be a better sample
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19
Quiz 01
1. What is the difference between a
mathematic and an econometric model?
2. “If the population is all UWA students, the
sample could be all Econ 2271 students. It
would, however, not be a good idea”. Why?
3. What is hypothesis test?
39
Workshop: Week One
5. Estimation of the model:
o
We obtain an equation: 𝑌 = 1.726 + 0.547X
o
𝑌 : estimated or fitted value of Y
o
1.726 = β1: an estimate of β1
o
0.547 = β2: an estimate of β2
o
Are these estimates close to the actual values?
– hypothesis test!
40
20
Workshop: Week One
6. Hypothesis test:
o
0.547 = β2: an estimate of β2
o
o
o
But this is just an estimate of the true β2
Another sample, another estimate
Maybe the true β2 is actually equal to 1 or our
finding may have been a chance occurrence
Perhaps the true β2 is 0.70
o
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Workshop: Week One
6. Hypothesis test:
o
o
A procedure by which we use the sample
information to reject / not reject an assertion
(hypothesis statement) about the population
parameters
Using the limited sample in our data set, we can
say something quantitative about what the true
parameters are / are not
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21
Workshop: Week One
6. Hypothesis test:
o
YW: Define hypothesis test
o
ChatGPT: “A hypothesis test is a statistical procedure used to
determine whether there is enough evidence to reject a null
hypothesis in favor of an alternative hypothesis”. “The null
hypothesis is a statement that assumes no relationship
between two variables or no difference between two groups,
while the alternative hypothesis is a statement that asserts
that there is a relationship or difference ” – doubtful!
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Workshop: Week One
6. Hypothesis test:
o
YW: Define hypothesis test in 1 sentence
o ChatGPT: “A hypothesis test is a statistical
procedure to test a claim or hypothesis about a
population using sample data”
o Empirically the “claim” or “hypothesis” is important!
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22
Workshop: Week One
Lecture Outline: Week One
• General information about ECON2271
• What is econometrics?
• Steps in economic /econometric analysis
• Revision
45
Workshop: Week One
Revision - Summations:
o
∑
𝑋
o ∑
o ∑
𝑋
𝑋
𝑋
⋯ 𝑋
𝑘
𝑛𝑘
𝑘𝑋
𝑘∑
o
∑
𝑋
𝑌
o
𝑋
∑
𝑋 : sample mean or average
o
𝑥
𝑋 𝑋: deviation of each
observation around the mean
o NB: 𝑥 𝑣𝑠 𝑋
𝑋
∑
𝑋
∑
𝑌
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23
Workshop: Week One
Revision – Variance:
o
Variance is a measure of how much variation
is exhibited by a random variable
o High (Low) variance - the variable
tends to (doesn’t) deviate much
o Is commonly used in addition to the
mean/expected value to describe how
random variables are distributed
47
Workshop: Week One
Revision – Variance:
o
o
Suppose that E(X ) = µ (not the same as the
error term in the regression)
The variance of the random variable X is
given by:
𝑉𝑎𝑟 𝑋
o
𝐸 𝑋
𝜇
𝜎
𝜎 is defined as the standard deviation of X
48
24
Quiz 02
1. ∑
𝑥
0. 𝑊ℎ𝑦? 𝑁𝐵: 𝑥 𝑣𝑠 𝑋
2. ∑
𝑥
∑
𝑋
𝑋 𝑋 . 𝑊ℎ𝑦?
49
Quiz 02
1. ∑
𝑥
=∑
𝑋 𝑋
2. ∑
𝑥
∑
𝑋
𝑋 𝑋 . 𝑊ℎ𝑦?
=∑
𝑋
𝑋 𝑋
𝑋
∑
𝑋
=∑
𝑋
𝑋 𝑋 ‐𝑋 ∑
𝑋
𝑋
=∑
𝑋
𝑋 𝑋
0. 𝑊ℎ𝑦? 𝑁𝐵: 𝑥
∑
𝑋
∑
𝑋
𝑋
𝑋
𝑛𝑋
𝑛𝑋
𝑋 𝑋 ‐∑
0
𝑋
𝑋 𝑋
50
25
Workshop: Week One
Revision – Variance:
o
Variance or standard deviation indicates how
closely or widely the individual X values are
spread around their mean value – a simple yet
very useful indicator!
51
Workshop: Week One
Topics for week two
o Four important distributions
o Linear regression models
52
26
Workshop: Week One
Q&A?
53
27