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 hadn’t 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 2001:09 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. Orthogonal, normal, slightly kurtotic Fitted values match actual, even 2001:09 No autocorrelation (Breusch-Godfrey) ARCH/GARCH not needed

13. Forecast (2007:03 – 2008:02) Peaks are trending upward The forecast seems to fit well

14. Forecast (2007:03 – 2008:02) Shown with 95% Confidence Interval

15. 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

16. 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