Average Domestic Airfare Presentation

Average Domestic Airfare Prices By, Meng Li Mrudal Tasgaonkar Viktoriia-Tetiana Karhina Rachael Haase

Transcript of Average Domestic Airfare Presentation

Page 1: Average Domestic Airfare Presentation

Average Domestic Airfare Prices

By, Meng Li Mrudal Tasgaonkar

Viktoriia-Tetiana KarhinaRachael Haase

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Executive Summary

● We Forecasted Average Domestic Airfare prices. It is important to be aware of for travel purposes.

● Source of data: Bureau of Transportation Statistics.

● Based on past average domestic airfare prices, our model predicts an increase in the airfare prices over the next 2 years.

● Our forecast predicts the airfare prices will raise to $406 by the fourth quarter of 2016.

Average Domestic Airfare Prices

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● Airfare series is nonstationary and not adjusted for inflation.● The series is adjusted for seasonality & has a general upward sloping trend● Cyclical Components: 2001 and 2007 recessions● Overall increase from 1996-01 as well as from 2009-14

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● Compared Domestic Airfare Prices to Real Disposable Income and Jet Fuel Prices● We predicted Real Disposable Income and Jet Fuel Prices are predictors for domestic

airfare prices since the more money people are making the more places they can visit and vacation to and the lower jet fuel prices are the cheaper it costs airlines.

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TS Decomposition● We need to consider the

possibility that our series is a Random walk with drift looking at the strong PAC at the first lag.

● φ <1 vs φ=1● If we have a unit root, a

shock to the system will permanently change the forecast.

● The Unit Root Test suggests that it is a random walk with 95% confidence.

● Need a Walk Off to make the final call

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Candidates For the Walk Off● Looking for ARMA fingerprints in the correlogram, We first

guessed AR(1) and ARMA(1,1)● The final candidates are: Trend + AR(2) vs First Difference + MA(1)● Determined by 1) T score of one added regressor. ( T score for AR


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In Sample Fit● 2) Actual vs theoretical graph● 3) Correlogram of residuals to see if the

residuals are close to white noise. (we have big P scores on the Q stat on the 20th lag.)

● Static in sample forecast, Trend +AR(2) has slightly better in sample fit (MAPE 1.66 vs 1.75)

MA(1) AR(2)

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The Walk Off● Forecast 2013Q1-2014Q4● Blue--Trend Reverting. Red--Constant

Growth Rate ● First difference +MA (1) model has a

better out of sample fit, the series behaves more like a random walk with drift.

● MAPE and Theil confirms the graph observation.

● No turning points,so the forecast looks decent. It will be different if we forecast before a recession.

● We will pick the MA(1) model since we are only forecasting 2 years into the future.

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VAR Model ● Comparing trend and first

difference model to pick the better one

● Trend model using airfare_sa income_sa fuel_sa c @trend

● Picked 2 lags for trend model● First Difference model using d

(airfare_sa) d(income_sa) d(fuel_sa)● Picked one lag for first difference

model● Effects of Real Disposable Income

and Jet Fuel Prices on Average Domestic Airfare Prices

Nonstationary data = spurious correlation

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VAR model● Income and fuel do granger cause

Domestic Airfare Prices shown by the individually low p-values.

● Neither airfare nor fuel granger cause real disposable income, together or individually...as expected.

● Income and fuel also granger cause Airfare together evident from the test results.

● Income does granger cause jet fuel prices but airfare does not have a significant effect on them. However, the two granger cause jet fuel prices.

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VAR Model● A positive shock in

income leads to a slightly upward revision in the airfare forecast which dies off in about 2 -3 periods.

● A positive shock in fuel leads to a slightly upward revision in the airfare forecast that dies off in about 4-5 periods.

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VAR Model● A 1Std.Dev increase in

the growth rate of income leads to a faster growth in the airfare prices.

● A 1Std.Dev increase in the growth rate of fuel leads to a fast growth in the airfare prices.

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Trend vs. 1st differenceAccording to Mape and Theil..

● 1st Difference predicts income better

● Trend predicts airfare and fuel better

● No definite answer ● Forecast isn’t very accurate

for either of them ● Not enough observations● Overprediction of income

due to lack of causality.

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Forecast VAR ARMA

VAR model is showing a slightly over optimistic forecast. Lack of data and imperfect granger causality.

ARMA is a more realistic model and shows us that the domestic airfare prices are going to increase in the next 2 years at the reasonable slope.

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According to the Bureau of Transportation Statistics there were 423 billion passenger miles flown on domestic flights through August 2015. This number is up 5% from the same period in 2014.That puts the industry on track for a record year.


2015 Prices

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External Factors →Economic- GDP, per capita income, disposable income, industrial production, level of business, and consumer confidence.

Other factors:

1. Political and Legal- regulations and restrictions, 2016 election.2. Social and Demographic- demand for flying in the long run. 3. Technological and Environmental- use of newest technology will decrease

the cost of airline operations (fuel cost).

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ForecastModel that we pick:

→ First difference ARMA model. Plus a slight upward tweak to our existing forecast due to a progressing economy.

Risk associated with our forecast:

→ Unpredicted recession or other potential unpredictable circumstances.

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Thank you! Questions, comments, suggestions?