ECON7350: Applied Econometrics for Macroeconomics
and Finance
Research Report
Due date: 10 May 2024, 13:59 (1:59pm)
Instructions
The project consists of three research questions. Please answer all questions as clearly,
concisely and completely as possible. Each question is worth 50 marks, for a total of 150
marks. This report will constitute 50% of your overall grade in this course.
We suggest that you use R for all empirical work involved. However, you should be able to
use another statistical software (e.g. Eviews, Stata, Python, etc.) without a problem. If you
do choose to work with an alternative software, please note that support for software-specific
issues from the course coordinator and tutors may be very limited.
Please upload your report via the “Turnitin” submission link (in the “Assessment / Re-
search Report” folder). Please note that hard copieswill notbe accepted. At the moment,
the due date is 1:59 PM on 10 May 2024, but please check BlackBoard regularly for announce-
ments regarding any changes to this. Your report should be a write-up of your answers (in
PDF format, single-spaced, and in 12 font size).
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You are allowed to work on this assignment with others, i.e., you can discuss how to
answer the questions with your classmate(s). However, this is not a group assignment, which
means that the report must be written individually: you must answer all the questions in
your own words and submit your report separately. The marking system will check for
similarities, and UQ’s student integrity and misconduct policies on plagiarismstrictly apply.
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Pleasedo notinclude or attach any software specific material such as R source code and output. In
particular, you should summarise the output in the report, but please do not copy-paste the “dump” produced
by the software. This “dump” is usually of poor quality in terms of presentation and so can cost you marks.
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Questions
The dataset for Questions 1 and 2 is contained inreport1.csv. The variables are quarterly
time-series of macroeconomic indicators in Australia for the period 1978Q3—2022Q4 (181
observations). In particular, the dataset contains the following variables:
∆y
t
(lnRPCGDP): change in the natural logarithm of seasonally adjusted gross domes-
tic product measured in current prices divided by Civilian population aged 15 years
and over (population) [this is a measure of economic growth];
u
t
(UNEMP): the seasonally adjusted unemployment rate for all persons averaged over
each quarter [this is a measure of unemployment];
π
t
(INF): the percentage change from the corresponding quarter of the previous year
of all groups CPI [this is a measure of inflation];
r
t
(OCR): the Interbank Overnight Cash Rate averaged over each quarter [the Reserve
Bank Board’s operational target for monetary policy];
r
3,t
(3Mth): 3-month Bank Accepted Bills/Negotiable Certificates of Deposit-3 months
averaged over each quarter [a money market rate];
r
5,t
(5Yr): Australian Government 5 year bond rate averaged over each quarter [a
capital market rate];
r
10,t
(10Yr): Australian Government 10 year bond rate averaged over each quarter [a
capital market rate].
1. Use the data provided to choose three (3) ARIMA(p,d,q) models for inflation,π
t
. Use
each of these three models to forecastπ
t
for 2023 and 2024 (two years or equivalently
eight quarters past the end of the sample). In doing so, please consider how such fore-
casts may be useful for policy, and consequently, ensure your inference is aligned with
the underlying motivation. Make sure to address all potential sources of uncertainty
on a conceptual level, and to the extent possible, quantitatively.
After a year, you are provided with the following data for Q1 to Q3 in 2023. Use these
figures to evaluate and discuss the relative forecast performance of your three models.
Quarter lnRPCGDP UNEMP INF OCR 3Mth5Yr10Yr
2023Q11.5343.5575.63.280 3.473 3.367 3.619%
2023Q2-1.5063.5913.23.797 3.937 3.158 3.437%
2023Q30.4753.6534.84.070 4.193 3.849 4.027%
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2. Use the data provided to obtain inference on the stability of the term structure of
interest rates. In particular, investigate the following questions:
(a) Is there evidence of nonstationarity in any of the four interest rates{r
t
,r
3,t
,r
5,t
,r
10,t
}?
(b) Are there any identifiable equilibrium relationships among the four interest rates?
(c) Are each of the following spreads stationary?
s
t,3−1
=r
3,t
−r
t
,
s
t,5−3
=r
5,t
−r
3,t
, and
s
t,10−5
=r
10,t
−r
5,t
Obtain inference the dynamic effects of shocks to the term structure of interest
rates, using the above interest rate spreadss
t,1−3
,s
t,3−5
, ands
t,5−10
, on inflation.
In particular, investigate the following questions:
(d) Ifs
t,3−1
increases by 1% and remains at the new level thereafter, what is the
expected effect (all else constant) on inflation,π
t
(i) at the time of impact, (ii)
one year after impact, (iii) 10 years after impact?
(e) Ifs
t,10−5
increases by 1% and remains at the new level thereafter, what is the
expected effect (all else constant) on inflation,π
t
(i) at the time of impact, (ii)
one year after impact, (iii) 10 years after impact?
Again, in answering these questions, please consider how the answers may be use-
ful for policy, and consequently, ensure your inference is aligned with the underlying
motivation. Also, please discuss the key assumptions underlying any conclusions you
obtain.
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3. The dataset for Question 3 is contained inreport2.csv. The variables are daily
time-series of two equity returns and a measure of market volatility for the period
4/01/1999—27/02/2024 (6408 observations). The dataset contains the following vari-
ables:
r
BHP,t
: natural logarithm of the change in the price of the BHP Group Limited
(BHP.AX) share price traded on the ASX;
r
CBA,t
: natural logarithm of the change in the price of the Commonwealth Bank
of Australia (CBA.AX) share price traded on the ASX;
p
V IX,t
: CBOE Volatility Index (
ˆ
VIX) form the Chicago Board Options Exchange
in USD [This is a measure of the level of volatility in the market].
(a) Use the data provided to obtain inference on the volatility ofr
BHP,t
andr
CBA,t
.
(b) Compare the estimates of volatility from your models in part (a) to thep
V IX,t
.
(c) Investigate the probability of a return less than 0.01% forr
BHP,t
andr
CBA,t
on
each of the days 28/02/2024, 29/02/2024 and 1/03/2024.
Again, in answering these questions, please consider how the answers may be useful for
risk management, and consequently, ensure your inference is aligned with the under-
lying motivation. Also, please discuss the key assumptions underlying any conclusions
you obtain.
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