Arthur Yang - s3329167 - Aviation Journal

AN ECONOMETRIC ANALYSIS OF QANTAS DOMESTIC AND JETSTAR
AIRWAYS
Arthur Yang
School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, Australia
Email: s3329167@student.rmit.edu.au
Abstract. This study focuses on determining the combination of demand determinants that affect passenger
demand in the form of revenue passenger kilometres (RPKs) for Qantas Domestic and Jetstar Airways using
multiple linear regression analysis. For model development, eight demand determinants identified in the
literature were selected as model parameters: Australia’s real GDP, real GDP per capita, air fares, Australia’s
population, unemployment, interest rates and bed spaces, and jet fuel prices. Through regression analysis,
three variables were found to impact passenger demand for Qantas Domestic, these were air fares represented
by IATA global airline yield, real GDP and unemployment. For Jetstar Airways, only two variables were
found to be statistically significant, these were real GDP per capita and real restricted economy air fares.
Both models were statistically significant with F statistics of 9.9 (Qantas) and 85.6 (Jetstar). The resulting
forecasts produced show an expected upward trend in passenger demand to 2020.
Keywords: air transport; econometric modelling, Australia, Qantas, Jetstar, forecasting methods
1. Introduction
Australia is heavily reliant on its air transport
system due to the country’s vast size and low
population density (Srisaeng et al. 2014). The
country’s geographical characteristics has largely
made the construction of surface substitutes, such as
high-speed railway networks, economically
infeasible, leaving air travel to be the dominant
transportation mode of choice (Fridström et al.
1989).
Qantas Airways is Australia’s national carrier
and its largest domestic carrier. The airline entered
the domestic market after acquiring Australian
Airlines in 1992, shortly after Australia abandoned
the “Two Airline Policy” and deregulated the
industry in 1990 (Srisaeng et al. 2014; Battersby et.
al 2001). More than a decade later, Qantas would
launch its low cost carrier (LCC) subsidiary, Jetstar
Airways, partly in response to Virgin Blue’s (now
Virgin Australia) success and aggressive growth in
the leisure travel market (Srisaeng et al. 2014). For
the majority of 2006 – 2015, the airline’s two-brand
strategy allowed it to compete in both the leisure
and business segments, and effectively dominate the
domestic market with around 65 percent market
share (Srisaeng et al. 2014; Qantas 2007).
Throughout this period, the airline faced many
challenges from the economic environment and
from within the air transport industry itself.
Economic challenges included the Global Financial
Crisis (GFC) of 2007-2008, the sluggish economic
growth post-GFC, high oil prices for much of the
decade, weakened consumer confidence, and a
strong currency that encouraged Australians to
travel overseas whilst discouraging foreign
travellers to visit Australia and fly domestically
(IBISWorld 2016). Industry challenges included
Virgin Blue’s transformation in 2011 from a low
cost carrier to a formidable full-service rival, and
the introduction of Tigerair Australia in 2007.
Qantas’ rivalry with the newly-formed Virgin
Australia lead to a brutal capacity war which
resulted in Qantas sustaining a well-publicised $2.8
billion loss in 2014. Whilst much of this ‘loss’
consisted of asset impairments, it nonetheless
highlighted substantial operating inefficiencies
within the company (Qantas 2014).
Since then, Qantas has been able to turn around
their financial performance due to their
“Transformation Program” and other favourable
economic conditions (Qantas 2015). If the airline
wants to continue its run of solid performance, it
will need to make effective operational and strategic
plans for the short- and long-term. In order to do
this, the company will need to accurately forecast
passenger demand.
Forecasting is the attempt to quantify demand
in a future time period (Wensveen 2011). Once an
airline has a notion of what future demand will be,
they can begin to plan the supply of services to meet
that demand (Doganis 2010). This includes
scheduling, fleet planning, route development,
product planning, maintenance planning,
determining station staffing and facility
requirements, pricing, and marketing and
advertising (Grosche et al. 2007; Doganis 2010;
Radnoti 2002). In essence, the decisions that result
from forecast figures have a far-reaching influence
on an airline’s operations, and impacts its ability to
offer competitive products whilst reducing
excessive capacity and unnecessary costs (Dozic et
al. 2015). The cost of getting a forecast wrong is
high. If a forecast undershoots actual demand, the

airline will lose customers, revenue and goodwill. If
the forecast overshoots actual demand, the airline
has to bear the real costs of flying underutilised
aircraft and operating other underutilised assets
(Holloway 2008).
Currently, the domestic market appears to be
stagnant. For the year ended December 2015,
Australian RPT airlines carried 57.49 million
passengers domestically, about the same number
since the end of 2013. Prior to that, growth was
uneven but averaged 2.8% per annum from 2006 –
2013 (BITRE 2006-2015). The pre-GFC growth
rates of six to seven percent look unattainable based
on recent trends. According to IBISWorld (2016), a
market research firm, Australian domestic airlines
are expected to generate $13.6 billion in revenue
and $914 million in profit for 2016. Revenue has
grown by 2.2% per annum from 2011 – 2016, but
this is expected to decrease to 1% from 2016 –
2021. As this growth rate is significantly lower than
the forecast growth in Gross Domestic Product
(GDP), this indicates that the Australian domestic
market has largely matured (Graham 2000). If this
forecast growth rate is accurate, then Qantas
Group’s domestic revenue is expected to grow at
only one percent per year, unless it is able to win
market share from its competitors. The Group’s
market share has been steadily declining since
Virgin Australia’s transformation and the
introduction of Tigerair Australia, Qantas currently
holds 60.8% of the market (IBISWorld 2016). In
order to increase its market share, the airline will
need to develop a sound strategic plan and deliver a
superior product. The airline’s ability to do this will
depend partly on the quality of their demand
forecasts.
The key objective of this study is to determine
the combination of exogenous and endogenous
variables that best model passenger traffic for
Qantas Domestic and Jetstar Airways for the period
of 2006 to 2015, and their respective weightings in
a multiple linear regression model. The model will
then be used to forecast traffic for Qantas and
Jetstar until 2020.
2. Air travel demand forecasting
2.1 Forecasting models
Airlines can choose from a range of methods and
techniques to forecast passenger demand. In the
literature, nearly all studies focus on quantitative
methods (Wang et al. 2010). Quantitative methods
use statistical data to analyse and forecast the future
behaviour of specific variables. Qualitative methods
on the other hand, use subjective techniques such as
opinions, surveys and beliefs to produce a forecast
(Vasigh et al. 2013).
Quantitative forecasting methods can be
broadly categorised as time-series analysis or causal
methods. Time-series analysis assumes that the way
traffic demand has behaved in the past will continue
to do so into the future (Ba-Fail et al. 2000). These
models are often used for shorter forecasts due to
their simplicity, using only one explanatory
variable, time. However, time-series models are
unable to identify the causes of market growth or
link market changes with expected changes of other
causative factors, such as income and price (Ba-Fail
et al. 2000).
Causal methods aim to find the link between
market changes and their causative factors. One of
the most popular causal methods used is regression
analysis (ICAO 2006). Regression analysis
produces a forecast based on one, or more,
explanatory variable/s that are considered to have a
causal relationship with the dependent variable
(ICAO 2006). Econometric modelling is the use of
multiple regression analysis using a price-income
structure to explain the demand for air travel (ICAO
2006; Holloway 2008). The underlying principle of
all econometric models is that passenger demand is
related to economic factors such as income, social
factors, and supply factors such as price (Doganis
2010). These models are often used to produce
long-term forecasts for large markets and are used
by major aviation bodies such as the International
Civil Aviation Organisation (ICAO), the
International Air Transport Association (IATA), the
Federal Aviation Administration (FAA), and
airliner manufacturers such as Boeing and Airbus
(Holloway 2008). Studies using econometric
models are numerous within the literature (Karlaftis
et al. 1996; Ba-Fail et al. 2000; Battersby et al.
2001; Abed et al. 2001; Bhadra 2003; Njegovan
2005).
Other popular quantitative forecasting methods
in the literature include gravity models (Fridström
et al. 1989; Grosche et al. 2007; Sivrikava et al.
2013) and artificial neural networks (Srisaeng et al.
2015b; Alekseev et al. 2009).
Qualitative forecasting methods are less
common in the literature, but some examples of
qualitative methods are focus groups, market
surveys, market experiments, barometric
forecasting, historical analogy, the Delphi method,
expert opinion and sales force opinion (Vasigh et al.
2013; Wensveen 2011).

2.2 Demand determinants
The demand for air transport depends on two main
groups of drivers, geo-economic drivers and
service-related factors (Wang et al. 2010).
Geo-economic factors are determined by
economic activity and the geographical
characteristics of the area where transportation takes
place (Wang et al. 2010). Geo-economic factors can
be further divided into activity factors and
locational factors (Jorge-Calderón 1997). Activity
factors relate to the commercial, industrial and
cultural activities in the market, encompassing
variables such as economic growth, personal
income, population, demography, unemployment,
interest rates, imports and exports, exchange rates,
and the fuel price (Fridstrom et al. 1989; Ghobrial
1993; Jorge-Calderón 1997; Ba-Fail et al. 2000;
O’Connell et al. 2005; Dempsey et al. 2006;
Radnoti 2002; Hanlon 2007; Chi et al. 2013;
Sivrikava et al. 2013). These activity-related
variables can be broken down into even more
specific variables, for example, income distribution,
percentage of population with university degrees,
percentage of full-time workers, employment
composition, and the economic, political and
cultural links between two markets (Grosche et al.
2007; Russon et al. 1993). Air transportation tends
to be very reactive to these economic fluctuations.
The most common locational factor is distance,
which affects demand in two different ways. Firstly,
it has a negative effect on demand as there is less
social and commercial interaction between
communities as distance increases. Secondly, it can
have a positive effect on air travel demand as
passengers are more likely to choose air transport
for longer journeys due to convenience (Hutchinson
1993; Jorge-Calderón 1997). In Australia, where the
distances between major urban hubs is large and the
number of viable surface transport substitutes is
few, air transportation plays a crucial role in
domestic travel (Srisaeng et al. 2014).
Service-related factors are the factors that
affect the quality of the product, and the price
charged for the product (Jorge-Calderón 1997;
Wang & Song 2010). Unlike geo-economic factors,
which are largely outside the control of airlines,
service-related factors are controlled of the airline
industry (Jorge-Calderón 1997; Grosche et al.
2007). Price is one of the most important factors
affecting passenger demand. Doganis (2010) states
that a significant amount of air travel growth since
1970 can be attributed to falling real prices in air
transport. Besides price, other supply conditions
such as flight frequency, seat availability, departure
and arrival times, number of en route stops, load
factor, aircraft size and technology, safety record
and airline image can affect demand (Battersby et
al. 2001; Doganis 2010; Wang et al. 2010).
Generally, leisure travellers and business
travellers are thought to have different demand
determinants and so, will be modelled
independently in this paper.
2.3 Leisure travellers
The leisure market contains two broad types of
travellers, those going on holiday and those visiting
friends or relatives (VFR). Leisure travellers pay for
their own tickets, whereas business travellers
generally do not, which leads to a number of
important differences between the leisure and
business market (Doganis 2010).
The most important geo-economic variable
affecting leisure travel demand is personal income
(Doganis 2010). Ideally, disposal income would be
used as an explanatory variable as it directly
represents the amount consumers have available to
spend on non-essential products like leisure travel.
However, as this data is often unavailable, many
studies use GDP per capita as a proxy for income.
One limitation of using GDP per capita is that it
does not take into account the distribution of
income, which can lead to somewhat misleading
results in markets with high income inequality.
Other macroeconomic measures besides GDP per
capita and GDP include inflation, unemployment,
interest rates and exchange rates. Overall, changes
to these variables affect the amount of disposable
income available to consumers. A negative
combination of these factors may rattle consumer
confidence leading to more saving and less
spending. Thus, leisure travellers will have less total
income available to spend and will be less inclined
to spend what they do have left, resulting in less
demand for air travel. A weaker currency may
increase demand by persuading Australians to
holiday domestically, and may attract foreign
tourists to visit and fly locally (IBISWorld 2016).
The total number of passengers and the
demographics of the market will impact demand.
An increase in the number of passengers may be
due to population growth or improvements in
connecting flights (Wensveen 2011). Age also plays
a significant factor in how people travel. O’Connell
et al. (2005) found that people under 24 generally
favoured LCCs, with a survey finding that 47% of
Air Asia’s passengers were under 24. The majority
of this group were travelling for non-business
purposes such as visiting friends and family, going
on holiday, or travelling to and from school. It was

the travellers’ parents that mostly paid for these
trips. Another segment of the population of interest
to LCCs is the over 55’s market. This group tend to
be wealthier, healthier than previous generations,
and most importantly, time-rich (Graham 2006).
Shaw et al. (2006) found that the propensity to
travel was highest at this age and remained
relatively high until people reached 75 years of age,
at which point there is a noticeable drop-off. These
findings will become more and more relevant to the
Australian domestic market as the average age of
the population increases.
Lifestyle and socio-economic factors also play
a role in determining demand in the leisure market.
Graham (2006) states that passenger demand is not
only influenced by determinants, but also by
motivators. Determinants are those factors that
make it possible for people to travel. They include
income, leisure time, price and quality (Graham
2000). On the other hand, motivators are those
factors that make the consumer willing to travel.
They are often related to personality traits and
attitudes, and may be influenced by advertising
(Graham 2000). Specific motivators include: the
attitude towards travel within a given society,
including curiosity about the world and its various
cultures and histories (Doganis 2010; Radnoti
2002); the education levels of the population; the
amount of leisure time available to consumers; the
increasing proportion of young, childless, multi-
earner families; and the number of wealthy retirees
in developed countries (Doganis 2010; Radnoti
2002; Hanlon 2007). Graham (2000) posits that
there will be limits to demand. Regardless of the
positive changes in demand determinants, Graham
states that at some point, leisure travellers will lose
the motivation to keep taking trips. According to the
law of diminishing marginal utility, the more leisure
trips travellers take, the less benefits received from
each subsequent trip and eventually, travellers may
choose to spend their money in other ways.
Finally, the most important service-related
factor for leisure travellers is price. Changes in
price has a large influence over leisure passengers
as they are responsible for paying for the air fares
out of their own pocket, and there are a wide range
of travel and non-travel substitute products
competing for the consumer’s budget (Brons et al.
2002).
2.4 Business travellers
Business travel is less dependent on factors
such as personal income, and more influenced by
overall economic growth within the market
(Doganis 2010). Income is less relevant as the
journey is not paid for by the business passenger
themselves, but by the firm instead (Doganis 2010).
For the same reason, corporate travellers also tend
to be less sensitive to changes in price than leisure
travellers, although, not as much as airlines had
once assumed. Bender et al. (1998) found there had
been a trend of business passengers becoming more
price-conscious due to factors such as the pressure
to reduce corporate travel costs, corporate
downsizing, and improving telecommunications
technologies.
Although price remains an important
determinant of demand, there are other various
supply conditions such as flight frequency, seat
availability (load factor), departure and arrival
times, and the number of stops (Doganis 2010).
These factors all relate to travel time and business
passengers are likely to be more time-sensitive than
leisure travellers. Travel time is often represented
as the difference between the desired departure time
of a passenger and the actual arrival time (Grosche
et al. 2007). The more frequent flights are, the more
chance the airline meets the traveller’s schedule,
and the more flexibility the airline offers in case the
traveller wishes to change flights (Vasigh et al.
2013). An airline’s on-time performance is another
consideration as flight delays increase travel times
(Grosche et al. 2007).
Airlines can also induce customer loyalty by
offering free flights and perks in exchange for
repeat business (Vasigh et al. 2013). This has been
especially successful with business passengers, who
may be willing to stick with a particular airline even
if they are not the cheapest. The design of ground
and inflight products also serves as an important
differentiator between airlines, which can lead to
brand loyalty (Holloway 2008). As business
passengers value productivity while travelling, they
may be willing to pay more for higher quality
service and flexibility (Brons et al. 2002).

3. Developing the forecast
From the literature review, eight relevant
explanatory variables were identified. These were
GDP, GDP per capita, air fares, airline yield,
population size, unemployment, interest rates, bed
spaces, and jet fuel prices. Monthly or quarterly
data for all variables were for the period of 2006 –
2015. Qantas Domestic and Jetstar Airways traffic
data were available from Qantas reports and
websites. The Australian Bureau of Statistics (ABS)
website housed data on real GDP, population,
unemployment and bed spaces. Interest rate data
was available from the Reserve Bank of Australia’s
(RBA) website. Real air fares for business class, full
economy, restricted economy and best discount
were found on the Bureau of Infrastructure,
Transport and Regional Economics (BITRE)
website. Global airline yield data were retrieved
from IATA reports and websites. Real jet fuel
prices were available from the United States Energy
Information Administration website.
Once the variables were selected and the data
collected, a correlation matrix (see Table 1) was
created to determine whether multicollinearity was
an issue. Multicollinearity occurs when two or more
of the independent variables are highly correlated,
which will result in statistically insignificant
coefficient estimates (Karlaftis et al. 1996). The
correlation matrix shows that Australia’s GDP and
population are highly correlated, which means that
one of the variables must be excluded from the
model, or combined into a variable known as GDP
per capita. The first option was chosen for Qantas
Domestic, which used GDP as an explanatory
variable, but not population. The second option was
chosen for Jetstar Airways, so that GDP per capita
could be used to represent income. The matrix also
shows that interest rates and unemployment are
likewise strongly correlated at -0.96. Due to this,
only one of these variables were tested in a model at
a time. When both variables were used in the same
model, the coefficients for one or both would be
statistically insignificant.
The model chosen to specify the functional
relationship between the explanatory variables and
the dependent variable was the multiplicative (log-
log) model. This model was chosen as it is suitable
for forecasting traffic at an aggregate level such as
global, or in this case, regional traffic flows (ICAO
2006). The mathematical form of the model is
represented below:
log (Y) = log (a) + b log X1 + c log X2 + …
+ z log Xn
Where Y is the dependent variable (RPKs), X1, X2
and Xn are the explanatory variables, and b, c and z
are coefficients.
Once all the data was collected, regression analysis
was performed using the Data Analysis ToolPak in
Microsoft Excel. After fitting the data and
establishing the value of the constant and the
coefficients, it was necessary to determine how
statistically sound the model was. The statistical
tests used for this purpose were the coefficient of
multiple determination (R-squared statistic), the
Student’s t-test and the F statistic. The R-squared
statistic measures the closeness of fit of the time-
series data to the regression model (Doganis 2010).
It explains how much of the variability in the
dependent variable is explained by the explanatory
variables (Karlaftis et al. 1996). Doganis (2010)

states that in order to use the model for forecasting
purposes, the R-square figure would ideally be
above 0.9, which represents a very good fit. As an
alternative to R-square, the F statistic can be used to
determine if the model is a sound representation of
reality, or an abnormality (Vasigh et al. 2013).
Finally, the t-statistic is a statistical measure of the
confidence that can be placed in the coefficient
estimate. The larger the t-statistic, the greater the
statistical significance of the relationship between
the independent variable and the dependent
variable, and the more confidence that can be
placed in the estimated value of the coefficient
(ICAO 2006).
For Qantas Domestic, the most statistically
significant model was:
log (Qantas Domestic RPK) = -11.521 - 0.655log
(Yield) + 1.559log (GDP) - 0.471log
(Unemployment Rate)
The signs for the coefficients for IATA Global
Airline Yield, GDP and Unemployment are as
expected. Airline yield increases due to increased
revenues per passenger, which are usually the result
of higher air fares. Higher air fares would
discourage the demand for air travel. Increases in
GDP usually indicate favourable economic
conditions and more business travellers. An
increase in unemployment is expected to dampen
demand for corporate travel and may be a sign of
overall economic weakness. All coefficients are
statistically significant with a t-statistic exceeding 2.
The most statistically significant coefficient was
GDP with a t-statistic of 4.4. Overall, the model is
statistically significant with an F statistic of 9.9.
However, the adjusted R-squared statistic of 0.43
was quite low, which indicates a high amount of
variation of the dependent variable was left
unexplained by the independent variables.
For Jetstar Airways, the most statistically
significant model was:
log (Jetstar Airways RPK) = -52.604 - 0.37log
(Real Restricted Economy Air Fare) + 6.405log
(GDP per capita)
The signs of the coefficients are as expected. As
income (GDP per capita) increases, it is expected
that people will spend more on luxury goods and
services such as leisure trips. Both coefficients were
statistically significant, exceeding a t-statistic of 2,
with the most statistically significant coefficient
being GDP per capita, with a t-statistic of 6.7.
Overall, the model had an F statistic of 85.6. The
adjusted R-squared statistic for this model was
better at 0.82, suggesting that 82% of the variation
in Jetstar Airways’ RPK figures were explained by
the two independent variables.
Once the models were found to be statistically
valid, they were tested by inputting the actual
values of the independent variables into the model,
and then comparing the predicted RPK values
generated by the model and the actual RPK values
that occurred. After these results were deemed
satisfactory, a forecast was produced by obtaining
the forecasted figures of the explanatory variables,
i.e. GDP, unemployment and population, and
entering them into the model. These values were
obtained from the International Monetary Fund’s
World Economic Outlook (April 2016) and the
ABS.

Fig. 1. Forecast of Qantas Domestic RPKs
Fig. 2. Forecast of Jetstar Airways RPKs

4. Discussion
4.1 Qantas Domestic model and forecast
There are a number of important limitations
associated with the Qantas Domestic model. Firstly,
the dependent variable incorporates RPK figures
from both Qantas Domestic, the domestic airline
aimed mainly at corporate travellers, and
Qantaslink, the regional airline. This is due to
Qantas combining the two figures in traffic reports
from June 2012 onwards. As of 2012, the regional
carrier made up 10 – 15% of the total amount of
Qantas Domestic RPKs. This is likely to make the
model less appropriate as the demand determinants
driving business travel and regional travel are
different. Next, whilst attempting to develop the
Qantas model, no price variable except IATA
Global Yield proved to be statistically significant. It
would have been preferable to use the yield for all
domestic Australian airlines instead for relevance,
but data for this was limited. Finally, GDP as an
economic variable is quite broad. A more preferred
variable may be commercial expenditure or
commercial investment. These variables may
capture the business climate more accurately and be
a better determinant of corporate travel demand.
Overall, these issues may have contributed to the
relatively low R-squared statistic of 0.43.
The forecast shows steady upward growth in
RPKs. This trend is mainly due to the expected
increase in GDP and fairly steady unemployment
figures. According to the IMF (2016), Australia’s
GDP is forecast to increase by 2.5% in 2016, 3% in
2017 and then 2.8% until 2021. As for
unemployment, the IMF forecasts unemployment to
remain steady at 5.8% until 2017 and does not make
further projections beyond that year. For the
purposes of this forecast, unemployment is assumed
to remain at 5.8%. Airline yield is also held
constant as there are no readily available
projections. As a result, the upward growth depicted
in the forecast is due almost solely to changes in
GDP. The fact that the forecast is driven by only
one variable is a major limitation and may result in
inaccurate forecast figures. This can be seen by
optimistic prediction that RPKs will reach 900
billion per quarter before 2020. This seems unlikely
given that Qantas Domestic has remained steady at
around 650 to 700 billion RPKs per quarter since
2008, and that the domestic market in Australia is
expected to grow at only one percent per year
(IBISWorld 2016). In order to improve the forecast,
it will be necessary to collect more detailed GDP
and unemployment projections.
4.2 Jetstar Airways model and forecast
The Jetstar Airways RPK model demonstrates that
price and income are the main determinants that
influence traffic. The GDP per capita variable is
used as a proxy for disposable income, which was
unavailable. The chief limitation of this model and
forecast may be its simplicity. Out of all the
explanatory variables identified in the literature,
only two were used. However, Karlaftis et al.
(1996) state that in their situation, there was no
evidence that increasing the number of variables
would include the quality of the forecast, and that
the more variables there are in a model, the higher
the uncertainty that surrounds the forecast.
The forecast shows a steady increase expected
for Jetstar Airways which seems to match their rate
of growth from 2006 – 2015. Once again, air fares
are held constant, which means that the only
variable influencing the forecast is GDP per capita.
As GDP is expected to grow faster than the
population (2.8% vs. 1.7%), GDP per capita is
expected to increase, and so are RPKs. It should be
noted that the large spike in RPKs from the fourth
quarter of 2006 to the second quarter of 2007 was
due to Qantas combining Jetstar Airways’ RPK
figures with the newly-formed Jetstar
International’s RPK figures in the monthly traffic
reports for that period.
5. Conclusions
Forecasting is one of the most important aspects of
airline planning and management. If Qantas is to
remain successful in the coming years, it needs to
develop accurate demand forecasts for operational
and strategic planning. This study sought to identify
the combination of exogenous and endogenous
variables that best model the passenger demand for
Qantas Domestic and Jetstar Airways’ products.
Out of the eight demand determinants identified in
the literature, only three were used in the Qantas
Domestic model and only two were used in the
Jetstar Airways model. Both models were
statistically significant, with F statistics of 9.9 for
Qantas Domestic and 85.6 for Jetstar Airways. The
models produced suggested that business travel in
Australia is driven by GDP and unemployment, and
that leisure travel is mainly determined by income,
which is consistent with the literature. However, the
Jetstar model also indicated that income had a
greater impact on passenger RPKs than price, which
contradicts the literature, stating that in developed
economies such as Australia, price has a far greater
effect than income in affecting leisure passenger
demand. The forecasts produced show upward

trends for both carriers, but the forecasts face many
limitations due to the fact that they were essentially
driven by changes in one variable only. Overall, the
application of econometric modelling to Qantas’
and Jetstar’s traffic figures has provided a better
understanding of the demand determinants that
affect passenger demand for the two carriers.
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