Revenue Passenger Miles (RPM)

Brandon Briggs, Theodore Ehlert, Mats Olson, David Sheehan,

Alan Weinberg

What are RPM?

• The leading indicator of the health of the airline industry

• Nearly universal application• Measures passenger traffic:

– Number of seats sold multiplied by distance traveled– Distances are fixed– Expands only by airline capacity– Accurately reflects changes in demand– Does not rely upon sales figures– Insulated from inflationary concerns

Characteristics of Airline Industry

• August is the peak month

• RPM always decline in September

• Highly cyclical

Effect of 9/11 on RPM

• Significant drop in September RPM– Air travel was shut down for several days– RPM bottomed out for several months post-9/11

• Long-term impact on RPM– RPM depressed below pre-9/11 levels for 3 years– What would the graph of RPM look like if 9/11 hadn’t occurred?

Histogram of RPM

• Not significantly different than normal• Multi-peaked

0

2

4

6

8

10

40000000 50000000 60000000 70000000

Series: RPMSample 1996:01 2007:02Observations 134

Mean 57051599Median 56654368Maximum 77796451Minimum 38601424Std. Dev. 8303230.Skewness 0.271510Kurtosis 2.551070

Jarque-Bera 2.771615Probability 0.250122

Correlogram of RPM

• Seasonal trend in PACF

• Possible cyclical trend ACF

Unit Root Test of RPM

• No unit root– Does not

approximate white noise

– Affected by large drop in 2001:09

• Add intervention variable (STEP)

Box-Jenkins Model I

• Step function– Parse data for pre-9/11

and post-9/11 trends to account for precipitous drop in RPM

• First difference• Seasonal difference

• Drop first difference– Negative coefficient on

autoregressive term– Over-differenced

Box-Jenkins Model II

SRPM = C + SSTEP + AR(1)

Box-Jenkins Model II

• Residuals still not orthogonal (Q-Stats)

• Add– MA(12)– MA(15)– AR(2)

Box-Jenkins Model III

Box-Jenkins Model III

• Orthogonal, normal, slightly kurtotic

• Fitted values match actual, even 2001:09

• No autocorrelation (Breusch-Godfrey)

• ARCH/GARCH not needed

Forecast (2007:03 – 2008:02)

• Peaks are trending upward

• The forecast seems to fit well

30000000

40000000

50000000

60000000

70000000

80000000

90000000

96 97 98 99 00 01 02 03 04 05 06 07 08

RPM RPMF

Forecast (2007:03 – 2008:02)

55000000

60000000

65000000

70000000

75000000

80000000

85000000

06:01 06:04 06:07 06:10 07:01 07:04 07:07 07:10 08:01

RPMRPMF

RPMF+2*SEFRPMF-2*SEF

Shown with 95% Confidence Interval

Long-term effects on RPM

• Added a linear trend from data 1996:01 – 2001:08

• Linear trend represents mean value for RPM if 9/11 did not occur

• RPM is trending at a lower mean post-9/11

• Post-9/11 trend has greater acceleration than pre-9/11, suggesting RPM is catching up

Conclusion

• RPM drops 29.8B from 2001:08 – 2001:09– Difficult to measure short-term impact of 9/11 on

demand as measured by RPM due to complete shut-down of airports

– May follow our study with daily RPM analysis• RPM drops 184.2B from 2001:09 – 2002:08

– Confidence Interval: +/-13.6B– 25% decline– Definite long-term impact of 9/11 on RPM– Does not accommodate impact of post-9/11 recession

*Multiply SSTEP by month and sum over twelve months