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

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Presentation transcript:

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

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

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

Forecast (2007:03 – 2008:02) 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