1. Will the Airline Industry Recover? Econ 240C Group E: Daniel Grund Daniel Jiang You Ren David Rhodes Catherine Wohletz James Young

2. Table of Contents Motivation Data Collection Identifying the Model Intervention Variables ForecastConclusions

3. Motivation Will the airline industry be able to recover after the September 11 th terrorist attack? What events significantly effect the airline industry post deregulation? What are forecasted revenue passenger miles?

4. Data Collection Bureau of Transportation Statistics Final Scheduled and Non-Scheduled Revenue Passenger Miles Monthly Data, January 1981-December 2003. –http://www.bts.gov/oai/ http://www.bts.gov/oai/

5. The Raw Data

6. Difference of Logs

7. AutocorrelationPartial CorrelationAC PAC Q-Stat Prob **|. | **|. | 1-0.293-0.293 23.894 0.000.|. |.|. | 2 0.036-0.055 24.257 0.000.|. |.|. | 3-0.020-0.027 24.367 0.000 *|. | *|. | 4-0.108-0.132 27.651 0.000.|** |.|* | 5 0.229 0.176 42.458 0.000 *****|. | *****|. | 6-0.687-0.661 176.06 0.000.|** | *|. | 7 0.254-0.094 194.45 0.000 *|. | **|. | 8-0.098-0.222 197.16 0.000.|. | **|. | 9-0.025-0.223 197.34 0.000.|. | ***|. | 10 0.023-0.405 197.49 0.000 **|. | ****|. | 11-0.214-0.492 210.72 0.000.|****** |.|*** | 12 0.825 0.399 407.92 0.000 **|. |.|* | 13-0.267 0.077 428.60 0.000.|* |.|* | 14 0.082 0.092 430.54 0.000.|. | *|. | 15-0.040-0.072 431.01 0.000.|. |.|* | 16-0.055 0.098 431.91 0.000.|* |.|. | 17 0.169-0.019 440.33 0.000 *****|. |.|. | 18-0.635 0.048 559.66 0.000.|** |.|. | 19 0.261 0.003 579.91 0.000 *|. |.|. | 20-0.115-0.004 583.87 0.000 Clearly there is a lot of structure to this data.

8. Basic Observations Clearly the series is highly regular, following a more or less constant cycle in the difference of logs until September 2001. After that time there are distinctive disturbances to that pattern. Obviously September 11 th was a major factor, but we found two other significant disturbances.

9. CoefficientStd. Errorz-StatisticProb. JAN-0.028213 0.009585-2.943498 0.0032 FEB-0.057530 0.008373-6.871120 0.0000 MAR 0.210592 0.008715 24.16362 0.0000 APR-0.060710 0.012821-4.735313 0.0000 MAY 0.017059 0.012830 1.329640 0.1836 JUN 0.085402 0.012386 6.895121 0.0000 JUL 0.059078 0.011623 5.082747 0.0000 AUG 0.034427 0.009840 3.498584 0.0005 SEP-0.208309 0.013356-15.59622 0.0000 OCT 0.040941 0.015114 2.708797 0.0068 NOV-0.077783 0.011069-7.026996 0.0000 DEC 0.037158 0.014168 2.622738 0.0087 AR(1)-0.477291 0.077416-6.165296 0.0000 AR(2) 0.282609 0.086150 3.280437 0.0010 SAR(12) 0.720921 0.082302 8.759497 0.0000 MA(2)-0.717934 0.061278-11.71602 0.0000 SMA(12)-0.488249 0.095803-5.096384 0.0000 Variance Equation C 0.000231 8.18E-05 2.823865 0.0047 ARCH(1) 0.517236 0.161033 3.211984 0.0013 GARCH(1) 0.199424 0.155043 1.286253 0.1984 Pre-9/11 Regression

11. AutocorrelationPartial CorrelationAC PAC Q-Stat Prob.|* |.|* | 1 0.100 0.100 2.3829.|* |.|* | 2 0.095 0.086 4.5398.|. |.|. | 3-0.002-0.019 4.5404.|. |.|. | 4 0.039 0.033 4.9030.|. |.|. | 5 0.025 0.021 5.0553 *|. | *|. | 6-0.070-0.083 6.2435 0.012.|* |.|* | 7 0.072 0.085 7.4850 0.024.|. |.|. | 8-0.014-0.017 7.5347 0.057 *|. | *|. | 9-0.064-0.082 8.5388 0.074.|. |.|. | 10-0.048-0.023 9.1086 0.105.|* |.|* | 11 0.094 0.116 11.292 0.080.|. |.|. | 12 0.060 0.035 12.171 0.095.|. |.|. | 13 0.026 0.014 12.343 0.137.|* |.|* | 14 0.098 0.092 14.753 0.098.|. |.|. | 15-0.005-0.043 14.759 0.141.|. |.|. | 16 0.014-0.005 14.806 0.192 *|. |.|. | 17-0.077-0.054 16.294 0.178.|. | *|. | 18-0.048-0.064 16.871 0.205.|. |.|. | 19-0.047-0.040 17.428 0.234.|. |.|. | 20 0.002 0.049 17.428 0.294.|. |.|. | 21 0.052 0.061 18.126 0.317.|. |.|. | 22 0.001-0.006 18.126 0.381 Correllelogram of Residuals: further ARMA terms could be used, but unnecessary.

12. AutocorrelationPartial CorrelationAC PAC Q-Stat Prob.|. |.|. | 1 0.037 0.037 0.3203.|. |.|. | 2-0.054-0.055 1.0010.|. |.|. | 3-0.003 0.002 1.0025.|. |.|. | 4-0.048-0.051 1.5431 *|. | *|. | 5-0.097-0.094 3.7911.|. |.|. | 6 0.008 0.009 3.8050 0.051.|. |.|. | 7-0.029-0.041 4.0135 0.134 *|. | *|. | 8-0.070-0.071 5.2164 0.157.|* |.|* | 9 0.104 0.098 7.8579 0.097.|. |.|. | 10 0.008-0.017 7.8745 0.163.|. |.|. | 11-0.049-0.041 8.4718 0.206.|. |.|. | 12-0.005-0.013 8.4770 0.292.|. |.|. | 13-0.028-0.038 8.6767 0.370.|. |.|. | 14-0.032-0.014 8.9370 0.443.|. |.|. | 15-0.021-0.035 9.0528 0.527.|* |.|* | 16 0.099 0.093 11.546 0.399.|* |.|* | 17 0.144 0.150 16.789 0.158.|. | *|. | 18-0.054-0.081 17.520 0.177.|. |.|. | 19-0.007 0.002 17.534 0.229.|. |.|. | 20-0.028-0.024 17.735 0.277.|. |.|. | 21-0.052-0.031 18.423 0.300 *|. | *|. | 22-0.075-0.058 19.883 0.280 Correllelogram of Squared Residuals: Appears to be adequately clean, due to GARCH.

13. Residuals are fairly normal.

14. Actual Growth versus Pre-9/11 forecasting.

15. Difference in recolored forecast versus actual (in 1000s of RPMs) 9/11 Troop deployment War begins

16. Disruptions to the forecast By observing the data we found that there were three obvious events which significantly shocked the airline industry, each followed by a one or two month recovery with rates of growth far from predicted values. These events were Sept 2001 (9/11), Dec 2002 (Deployment of troops) and March 2003 (Commencement of war with Iraq). During the deployment of troops, US commercial airlines were used to move troops and equipment. The following two months were a return to equilibrium. The commencement of war with Iraq brought additional concerns of terrorist attacks, but these concerns faded.

17. CoefficientStd. Errorz-StatisticProb. EVENT-0.354932 0.023675-14.99178 0.0000 AFTEREVENT 0.095437 0.013862 6.884513 0.0000 PREEVENT2 0.094406 0.086043 1.097192 0.2726 EVENT2-0.258901 0.096246-2.689993 0.0071 AFTEREVENT2 0.142701 0.050712 2.813980 0.0049 EVENT3-0.238504 0.024301-9.814738 0.0000 AFTEREVENT3 0.246463 0.021573 11.42453 0.0000 JAN-0.033452 0.009200-3.636265 0.0003 FEB-0.058835 0.007390-7.961657 0.0000 …………… NOV-0.081920 0.008804-9.304688 0.0000 DEC 0.051670 0.009952 5.192004 0.0000 AR(1)-0.394049 0.068435-5.758039 0.0000 AR(2) 0.360461 0.091758 3.928399 0.0001 SAR(12) 0.597508 0.106091 5.632041 0.0000 MA(2)-0.745415 0.075285-9.901220 0.0000 SMA(12)-0.370395 0.115133-3.217091 0.0013 Variance Equation C 0.000327 0.000128 2.548138 0.0108 ARCH(1) 0.372624 0.133946 2.781891 0.0054 GARCH(1) 0.151924 0.209176 0.726295 0.4677 December 2002 (PREEVENT2) was not statistically significant but had some explanatory power. All other events were significant.

18. Event variables capture the effects of major events in recent years.

19. Residuals appear to be adequately clean. AutocorrelationPartial CorrelationAC PAC Q-Stat Prob.|* |.|* |10.0730.0731.3918.|* |.|* |20.0810.0763.1464.|. |.|. |3-0.029-0.0413.3714.|. |.|. |40.0290.0283.5937.|. |.|. |50.0280.0303.8083 *|. | *|. |6-0.074-0.0855.27280.022.|. |.|. |70.0320.0415.54430.063.|. |.|. |8-0.0080.0015.55990.135 *|. | *|. |9-0.078-0.0947.21970.125 *|. | *|. |10-0.078-0.0598.86980.114.|* |.|* |110.0840.11510.8000.095.|. |.|. |120.0200.00110.9110.143.|. |.|. |130.009-0.00610.9330.206.|* |.|* |140.0750.09612.5080.186.|. | *|. |15-0.031-0.06112.7690.237.|. |.|. |160.0270.00512.9800.295 *|. |.|. |17-0.072-0.03714.4230.275.|. |.|. |18-0.003-0.02114.4260.345.|. |.|. |190.000-0.00514.4260.418.|. |.|. |200.0250.05414.6000.481

20. Residuals Squared also clean. AutocorrelationPartial CorrelationAC PAC Q-Stat Prob.|. |.|. |10.0240.0240.1464.|. |.|. |2-0.032-0.0330.4198.|. |.|. |3-0.012-0.0110.4591.|. |.|. |4-0.044-0.0450.9766 *|. | *|. |5-0.088-0.0873.0624.|. |.|. |60.0210.0223.18070.075.|. |.|. |7-0.033-0.0413.46800.177.|. |.|. |8-0.039-0.0403.87360.275.|* |.|* |90.0750.0685.40760.248.|. |.|. |10-0.022-0.0355.53730.354.|. |.|. |11-0.045-0.0406.08220.414.|. |.|. |120.0190.0116.17690.519.|. |.|. |13-0.006-0.0106.18600.626.|. |.|. |14-0.016-0.0056.25330.714.|. |.|. |15-0.009-0.0236.27400.792.|* |.|* |160.0880.0888.45340.672.|* |.|* |170.1080.11311.7070.469.|. |.|. |18-0.033-0.04812.0220.526.|. |.|. |19-0.0110.00012.0570.602.|. |.|. |20-0.023-0.01012.2130.663

21. Residuals are normal.

22. The Pre-9/11 forecast and event model forecast give almost identical results for 2004 growth rates. This suggests that no further recovery from 9/11 is expected.

23. Conclusion Airline industry not expected to recover to pre-September 11 th, 2001, growth path by the end of 2004. Airline growth rates appear to have returned to their original pattern except for additional shocks due to Middle-East current events, but the industry appears to have permanently lost at least 5 million RPMs per month.

24. QUESTIONS?