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Revenue Passenger Miles (RPM)

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Title: Revenue Passenger Miles (RPM)


1
Revenue Passenger Miles (RPM)
  • Brandon Briggs, Theodore Ehlert, Mats Olson,
    David Sheehan, Alan Weinberg

2
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

3
Characteristics of Airline Industry
  • August is the peak month
  • RPM always decline in September
  • Highly cyclical

4
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
    hadnt occurred?

5
Histogram of RPM
  • Not significantly different than normal
  • Multi-peaked

6
Correlogram of RPM
  • Seasonal trend in PACF
  • Possible cyclical trend ACF

7
Unit Root Test of RPM
  • No unit root
  • Does not approximate white noise
  • Affected by large drop in 200109
  • Add intervention variable (STEP)

8
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

9
Box-Jenkins Model II
  • SRPM C SSTEP AR(1)

10
Box-Jenkins Model II
  • Residuals still not orthogonal (Q-Stats)
  • Add
  • MA(12)
  • MA(15)
  • AR(2)

11
Box-Jenkins Model III
12
Box-Jenkins Model III
  • Orthogonal, normal, slightly kurtotic
  • Fitted values match actual, even 200109
  • No autocorrelation (Breusch-Godfrey)
  • ARCH/GARCH not needed

13
Forecast (200703 200802)
  • Peaks are trending upward
  • The forecast seems to fit well

14
Forecast (200703 200802)
Shown with 95 Confidence Interval
15
Long-term effects on RPM
  • Added a linear trend from data 199601 200108
  • 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

16
Conclusion
  • RPM drops 29.8B from 200108 200109
  • 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 200109 200208
  • 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
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