THE UNIVERSITY OF NEW SOUTH WALES
SCHOOL OF ECONOMICS
TERM 1 2024
ECON2209 BUSINESS FORECASTING
FINAL EXAMINATION
1. Time Allowed: 24 Hours.
2. This is a Take-Home Exam. Your responses must be your own original work.
You must attempt this exam by yourself, without any help from others. By
submitting this exam you are declaring that you have NOT worked, collaborated,
or colluded with any other persons in the formulation of your responses. The
work that you are submitting is your OWN work.
3. Release date/time (via Moodle):
Tuesday, 30 April 2024, 8:00am (Australian Eastern Standard Time)
4. Submission date/time (Via Turnitin):
Wednesday, 1 May 2024, 8:00am (Australian Eastern Standard Time)
5. Submission details:
• A submission link is on the Moodle site under Final Exam.
• Submit your answer document in PDF format. Your file name should
follow this naming convention:
FE_your first name_zID_your last name_ECON2209.pdf
For example: FE_John_z1234567_Smith_ECON2209.pdf
• You get one opportunity to submit your file. Make sure that you submit
the file that you intend to submit.
• Your submitted answers should include the R code that you used.
6. Failure to upload the exam by the submission time will result in an immediate
late penalty of 5% from 8:01am on 1 May, followed by additional penalties of 5%
per calendar day or part thereof. Submissions will not be accepted after 5 days
(120 hours) of the original deadline.
7. This Examination Paper has 8 pages: 2 cover pages, 4 pages with questions,
and 2 pages describing the marking scheme.
PAGE 2 OF 2
8. Answer both questions.
9. Total marks available: 55 marks. This examination is worth 55% of the total
marks for the course.
10. Questions are not of equal value. Question 1 is worth 20 marks, Question 2 is
worth 30 marks, and 5 marks are awarded for presentation. The marking
scheme is explained on the last two pages of the exam paper.
11. This Take-Home Exam paper cannot be copied, forwarded or shared.
12. Students are reminded of UNSW’s rules regarding Academic Integrity and
Plagiarism. Plagiarism is a serious breach of ethics at UNSW and is not taken
lightly. For details see Examples of plagiarism.
13. This Take-Home Exam is an open book/open web exam. Further information is
available by clicking here.
• You are permitted to refer to your course notes, any materials provided by
the lecturer, books, journal articles, or tutorial materials.
• You are required to cite your sources and attribute direct quotes
appropriately when using external sources (other than your course
materials).
• It is sufficient to use in-text citations that include the following information:
the name of the author or authors; the year of publication; the page number
(where the information/idea can be located on a particular page when
directly quoted), For example, (McConville, 2011, p.188).
• When citing Internet sources, please use the following format: website/page
title and date.
• If you provide in-text citations, you MUST provide a Reference List.
14. Use of AI tools such as ChatGPT are prohibited. In cases where use is
detected, it will result in 0 marks for “Interpretation of the results,
arguments used and conclusions drawn”. It may also result in a referral to
the Academic Integrity Committee.
15. Use the methods and R packages taught in this course. Failure to do so
will result in a mark of 0 for Suitability of Methods.
16. Students are advised to read the exam paper thoroughly before commencing.
17. The Lecturer will be available online (via Moodle) after the exam paper is
released for a period of two hours.
UNSW ECON2209 Assessment
Final Exam
2024
Remember to type ‘library(fpp3)‘ in the R Studio Console before you start.
Question 1 [20 marks]
This question uses data from the Australian Bureau of Statistics (ABS) on the Australian labour force, from
ABS Catalogue 6202.0, Labour Force, Table 1.
Use the R package readabs to read in the data from the ABS website, as follows.
library(readabs)
lfs_1 <- read_abs("6202.0" , tables = 1, release_date = "2024-02-01") %>%
mutate ( Month = yearmonth(date)) %>%
as_tsibble(
index = Month,
key = c (series_id)
) %>%
filter(!is.na (value))
You must use the following method for selecting your data series.
Use the seven digits of your UNSW student ID to get the official seasonally adjusted data series
that you will use in this question, as in the following example for the case when your student ID
is z1234567:
set.seed(1234567)
myseries_sa <- lfs_1 %>%
filter(series_type == "Seasonally Adjusted") %>%
filter(`series_id` == sample(`series_id` , 1), year(Month)>=2000)
Use the following commands to get the corresponding original (non-seasonally adjusted) data
series:
myseries <-lfs_1 %>%
filter(series_type == "Original")%>%
filter(series == myseries_sa$series[1], year(Month)>=2000)
Make a note of the IDs of your two data series, in case you run into computer problems and need
to retrieve the series manually:
list(myseries$series_id[1], myseries_sa$series_id[1])
1
Plot your original (non-seasonally adjusted) data and seasonally adjusted data together using the
following code, putting an appropriate label for your series on the y-axis (i.e. fill in the blank in y
= " "):
myseries %>%
autoplot(value) +
autolayer(myseries_sa, .vars = value, colour = "red") +
labs(y = " ",
title = myseries$series[1],
subtitle = "Original (black) and Seasonally Adjusted (red)")
Tasks
Do not transform your data.
a. Explore the original (non-seasonally adjusted) data series using the following graphics
functions, being sure to discuss what you find from each plot: gg_season(), gg_subseries(),
gg_lag() and ACF() %>% autoplot()
b. Consider the last ten years (i.e. the last 120 months) of your data. Use an STL decomposition
of the orginal data (using the default settings) and produce a standard decomposition plot
showing the original data and its trend-cycle, seasonal and remainder components. Discuss
what you find from the decomposition plot.
c. Plot your seasonally adjusted data from the STL decomposition together with the official
seasonally adjusted data for the last ten years. What observations can you make about the
respective series?
d. Apply Holt-Winters’ multiplicative method with multiplicative errors to the seasonally
adjusted data from the STL decomposition. Then try the method making the trend damped.
Plot and compare the 24-month ahead point forecasts and prediction intervals for both
methods. Using accuracy(), compare the RMSE of the of the two methods; this comparison
is based on one-step-ahead in-sample forecasts. Which method do you prefer and why?
Be sure to label all your figures.
2
Question 2 [30 marks]
This question uses data from the Australian Bureau of Statistics (ABS) on Consumption and Income, from
5206.0 Australian National Accounts: National Income, Expenditure and Product, Table 1. Key National
Accounts Aggregates
• Our Consumption variable is “Household Final Consumption Expenditure: Chain volume measures -
Percentage changes” (series ID A2302449W)
• Our Income variable is “Real net national disposable income: Chain volume measures - Percentage
changes” (series ID A2302702R)
Download the data from the ABS website and create a tsibble aus_change using the following commands:
library(readabs)
consumptionData <- read_abs_series("A2302449W", release_date = "2023-12-01") %>%
mutate(Quarter = yearquarter(date)) %>%
as_tsibble(
index = Quarter
) %>%
filter(year(Quarter)>=1990) %>%
select(value) %>%
rename(Consumption = value)
incomeData <- read_abs_series("A2302702R", release_date = "2023-12-01") %>%
mutate(Quarter = yearquarter(date)) %>%
as_tsibble(
index = Quarter
) %>%
filter(year(Quarter)>=1990) %>%
select(value) %>%
rename(Income = value)
aus_change <- left_join(consumptionData, incomeData) %>%
relocate(Quarter)
Tasks
Do not transform your data.
a. Plot both Consumption and Income in the same figure. Using this single figure, discuss the
characteristics of the two data series individually and relative to each other.
b. Create four dummy variables (dum1, dum2, dum3 and dum4) as follows:
• dum1 is equal to 1 only in quarter 2 of 2020, and is otherwise equal to zero.
• dum2 is equal to 1 only for quarters 3 and 4 of 2020, and is otherwise equal to zero.
• dum3 is equal to 1 only for quarter 3 of 2021, and is otherwise equal to zero.
• dum4 is equal to 1 only for quarter 4 of 2021, and is otherwise equal to zero.
Explain why these dummy variables may help us model the relationship between
Consumption and Income.
c. Fit a time series regression model for Consumption, using Income and seasonal dummy
variables as predictor variables. Call this model1. Then add the dummy variables created in
Question 2 task b to the list of predictor variables. Call this model2. Report and discuss the
regression coefficients from both estimated models. For each model, plot the Consumption
data series and the fitted values on the same figure and discuss. That is, there should be
3
one figure for each of the two regression models. For each model, comment on the residuals,
using the standard plots (i.e. gg_tsresiduals()). Report the AICc for each model. Explain
which model you prefer and why.
d. Estimate model2 using dynamic regression (instead of time series regression), using no seasonal
ARIMA components. Explain why using dynamic regression might be an appropriate thing
to do in this case. Report and comment on the coefficient estimates, and the standard
residual plots.
e. Using the dynamic regression model from Question 2 task d, forecast 20 quarters ahead
assuming that income will continue to grow at 8% per quarter, the same rate as in the last
observation for Income in the data (2023 Q4). Call this the “High” scenario. Now assume
that income will grow at its mean rate of 1% per quarter. Call this the “Mean” scenario.
On the same figure as the actual Consumption data from 2015, plot the point forecasts and
prediction intervals generated under these two alternative assumptions. Discuss the relative
forecasts from the two different scenarios.
Be sure to label all your figures.
4
Marking for Question 1: Marks are awarded by overall achievement against the following criteria:
(a) Suitability of methods. 10 marks:
• 0 marks: Little or no attempt, or use of methods and R packages not taught in this course.
• 2 marks: Inappropriate methods used or methods inappropriately implemented.
• 5 marks: An attempt has been made to answer the question using methods that are appropriate and
appropriately implemented.
• 7 marks: A reasonable attempt at the question that generally follows the recommended solutions.
• 8.5 marks: Systematic analysis.
• 10 marks: All methods appropriate and appropriately implemented.
(b) Interpretation of the results, arguments used and conclusions drawn. 10 marks
• 0 marks: Little or no attempt, or use of AI detected.
• 2 marks: Little attempt to discuss the results, or a poor understanding of the results found.
• 5 marks: An attempt has been made to understand and explain all the results.
• 7 marks: Systematic and sensible discussion of all results.
• 8.5 marks: Discussion of the results seems correct and insightful.
• 10 marks: Insightful discussion beyond what might reasonably be expected, possibly drawing on external
references and other research.
Maximum marks for Question 1: 20
5
Marking for Question 2: Marks are awarded by overall achievement against the following criteria:
(a) Suitability of methods. 15 marks:
• 0 marks: Little or no attempt, or use of methods and R packages not taught in this course.
• 2 marks: Inappropriate methods used or methods inappropriately implemented.
• 5 marks: A partial attempt has been made using some methods that are appropriate and appropriately
implemented.
• 7.5 marks: An attempt has been made to answer all parts of the question using methods that are
appropriate and appropriately implemented.
• 10 marks: A reasonable attempt at the question that generally follows the recommended solutions.
• 12.5 marks: Systematic analysis.
• 15 marks: All methods appropriate and appropriately implemented.
(b) Interpretation of the results, arguments used and conclusions drawn. 15 marks
• 0 marks: Little or no attempt, or use of AI detected.
• 2 marks: Little attempt to discuss the results, or a poor understanding of the results found.
• 5 marks: An attempt has been made to understand an explain some of the results.
• 7.5 marks: An attempt has been made to understand and explain all the results.
• 10 marks: Systematic and sensible discussion of all results.
• 12.5 marks: Discussion of the results seems correct and insightful.
• 15 marks: Insightful discussion beyond what might reasonably be expected, possibly drawing on external
references and other research.
Maximum marks for Question 2: 30
Marking for Presentation of Exam Answers
(c) Presentation: Appropriate style of graphs, tables, reporting and clarity of writing. 5 marks
• 0 marks: Little or no attempt.
• 1 marks: Difficult to follow what has been done. Small font making graphs and tables hard to read.
Lack of clear writing.
• 2 marks: Presentation of results falls short of the standard in the provided solutions for tutorial exercises,
problem sets and course project.
• 3 marks: Presentation of results consistent with the standard in the provided solutions for tutorial
exercises, problem sets and course project.
• 4 marks: More polished presentation.
• 5 marks: Excellent presentation. Tables can still be in R output format - reformatting not required.
Maximum marks for Presentation: 5
Maximum marks for the Exam: 55