Econometric Analysis of Qantas Domestic and Jetstar AirwaysArthur Yang

School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, VIC 3001, Australia

Abstract

The aim of this project is to develop an econometric model that demonstrates the

relationship between endogenous and exogenous factors, and the demand for air

travel in the Australian domestic market. Based on the literature, eight

appropriate independent variables were identified: Gross Domestic Product

(GDP), GDP per capita, air fare prices, population size, unemployment rate,

interest rates, bed spaces, and jet fuel prices. Through multiple regression

analysis, these variables were used to build models to predict Qantas Domestic

revenue passenger kilometres (RPKs) and Jetstar Airways RPKs. For Qantas, the

most important explanatory variables were IATA global airline yield which

measures airline profitability, GDP and the unemployment rate. For Jetstar, only

price (Real Restricted Economy air fare) and income (GDP per capita) were

statistically significant. The forecasts developed from the models face several

limitations, but show growth due to rising GDP and GDP per capita. Overall the

research affirms findings from the literature that price and income play a primary

role in determining air travel demand whilst other macroeconomic and social

factors play a smaller role.

1. IntroductionEconometric models have been used for decades to explain or forecast air traffic

demand (Wang & Song 2010). These models make use of historical data through

statistical regression analysis, which allows measurements of the relationship

between demand and its determinants (Wang & Song 2010). This project uses

quarterly traffic, air fare, and macroeconomic data from 2006-2015 to develop an

appropriate model to be used in forecasting.

Econometric models are useful because they can be used as a forecasting tool.

Accurate forecasts are essential to an airline’s strategic plans and its ultimate

financial performance. Forecasts assist airlines in scheduling, developing fleet

requirements, route development, product planning, ascertaining station staffing

and facility requirements, pricing, marketing and advertising (Grosche, Routhlauf

& Heinzl 2007; Doganis 2010; Radnoti 2002).

Qantas Group has experienced significant highs and lows throughout the decade

of 2006-2015. The company has faced slowing economic growth, rising oil prices

for much of the period, weakened consumer confidence, and a strong Australian

dollar which encouraged more Australians to travel overseas, and less foreign

travellers to visit and fly domestically within Australia. Faced with such a

challenging and capricious environment, Qantas’ ability to develop a sound

strategic plan and deliver a superior product will depend partly on the quality of

their demand forecasts. This project aims to develop those forecasts through

econometric modelling.

2. Research Questions1. How much do macroeconomic factors and air fare prices impact passenger

demand for Qantas Domestic’s services?

2. How much do macroeconomic factors and air fare prices impact passenger

demand for Jetstar Airways’ services?

3. MethodologyThe econometric model was developed using the ICAO Manual of Air Traffic

Forecasting (2006) model. The main steps are:

Final model equations

Qantas Domestic

log (Qantas Domestic RPK) = log(-11.521) + -0.655log (Yield) + 1.559log (GDP) + -

0.471log (Unemployment Rate)

Jetstar Airways

log (Jetstar Airways RPK) = log(-52.604) + -0.37log (Real Restricted Economy Air

Fare) + 6.405log (GDP per capita)

5. DiscussionThe final models produced for both Qantas Domestic and Jetstar Airways are relatively

simplistic. Of the eight explanatory variables identified from the literature review, the

Qantas model used three: IATA Global Yield, GDP and Unemployment; whilst the

Jetstar model used two: Real Restricted Economy Air Fare and GDP per capita.

Whilst attempting to develop the Qantas model, no price variable except IATA Global

Yield proved to be statistically significant. It would have been more preferable to use

the yield for all domestic Australian airlines instead for relevance, but data for this was

limited.

The forecasts produced from the models have some limitations. For the Qantas model,

yield was held constant, and forecast GDP and unemployment figures were retrieved

from the International Monetary Fund’s World Economic Outlook. Real GDP is

expected to grow at 2.5% in 2016, 3% in 2017, and then 2.8% until 2021. The Outlook

forecasts unemployment only until 2017 and expects it to remain steady at around

5.8%. Therefore the majority of the growth depicted by the forecast is driven by GDP

changes only.

The forecast for the Jetstar model faces similar issues. Holding air fares constant, the

only variable influencing the forecast is GDP per capita. As GDP is expected to grow

faster than the population (2.8% to 1.7%), the forecast growth in Jetstar’s RPKs is

derived solely from the expected growth of Australia’s GDP per capita.

Dr. Graham WildLecturer and Project Supervisor

RMIT Aviation

& Aerospace

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Qantas Domestic RPK Forecast

Actual RPKs Forecast RPKs

1. Define the

problem

The desired forecast variable are RPKs for Qantas Domestic

and Jetstar Airways. The time horizon for the forecast will

be five years.

2. Select the

relevant causal or

explanatory

variables

The explanatory variables selected are: Gross Domestic

Product (GDP), GDP per capita, air fare prices, population

size, unemployment rate, interest rates, bed spaces, and jet

fuel prices

3. Collect data

for those

variables

Data were collected from various online sources, including

the: Qantas Investor Relations Website, IMF, BITRE, ABS,

RBA, and the US Energy Information Administration. Check

for multicollinearity by producing a correlation matrix and

exclude those explanatory variables that have high

multicollinearity.

4. Formulate the

model

The type of functional relationship between the dependent variable

and the selected explanatory variables need to be specified. The

ICAO Manual of Air Traffic Forecasting provides several

alternative mathematical forms that can be used. The main form

used for the project was multiplicative or log-log.

log 𝑌 = log 𝑎 + 𝑏 log𝑋1 + 𝑐 log𝑋2 +⋯+ 𝑧 log𝑋𝑛

5. Carry out an

analysis to test the

relationship being

hypothesised

Regression analysis was carried out in Microsoft Excel using the

Data Analysis ToolPak, which provided all the information. The

program provided summary statistics on the model coefficients,

their magnitudes and signs and statistical measures.

6. Establish the

model in the final

form

The best model is chosen after considering goodness of fit and the

statistical significance of the coefficients of the explanatory

variables.

7. Develop

forecasts of future

scenarios

In order to develop forecasts for the dependent variable, it is

necessary to obtain forecasts for the explanatory variables.

Forecasts of the explanatory variables were obtained from the

IMF’s World Economic Outlook publication.

4. Results

Dependent variable scatter plots

Explanatory variable bar charts/scatter plots

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Actual RPKs Forecast RPKs

6. ConclusionBased on the research, macroeconomic variables such as GDP and unemployment

play a much larger role in the demand for Qantas Domestic’s services than air

fares, which is somewhat expected from the literature.

For Jetstar, it also appears that macroeconomic variables such as income (GDP per

capita) play a greater role in driving demand than air fares. This seems to

contradict the literature, which states that in developed countries in Australia,

demand for leisure travel is driven by changes in price more than changes in

income.

ReferencesDoganis, R 2010, Flying Off Course: Airline economics and marketing, 4th edn, Routledge, Oxon.

Grosche, T, Routhlauf, F & Heinzl, A 2007, ‘Gravity models for airline passenger volume estimation’, Journal of Air Transport

Management, vol. 13, no. 4, pp. 175-183.

International Civil Aviation Organisation 2006, Manual of Air Traffic Forecasting, International Civil Aviation Organisation,

viewed 8 June 2016, available at

<http://www.icao.int/MID/Documents/2014/Aviation%20Data%20Analyses%20Seminar/8991_Forecasting_en.pdf>.

Radnoti, G 2002, Profit Strategies for Air Transportation, McGraw-Hill, United States.

Wang, M & Song, H 2010, ‘Air Travel Demand Studies: A Review’, Journal of China Tourism Research, vol. 6, no. 1, pp. 29-49.