Will the Airline Industry Will the Airline Industry Recover?Recover?

Econ 240CEcon 240C

Group E:

Daniel GrundDaniel Jiang

You RenDavid Rhodes

Catherine WohletzJames Young

Table of ContentsTable of Contents

MotivationMotivation

Data CollectionData Collection

Identifying the ModelIdentifying the Model

Intervention VariablesIntervention Variables

ForecastForecast

ConclusionsConclusions

MotivationMotivation

Will the airline industry be able to recover Will the airline industry be able to recover after the September 11after the September 11thth terrorist attack? terrorist attack?

What events significantly effect the airline What events significantly effect the airline industry post deregulation? industry post deregulation?

What are forecasted revenue passenger What are forecasted revenue passenger miles?miles?

Data CollectionData CollectionBureau of Transportation StatisticsBureau of Transportation Statistics

Final Scheduled and Non-Scheduled Final Scheduled and Non-Scheduled Revenue Passenger MilesRevenue Passenger Miles

Monthly Data, January 1981-December Monthly Data, January 1981-December 2003.2003.– http://www.bts.gov/oai/http://www.bts.gov/oai/

The Raw DataThe Raw DataRevenue Passenger Miles (RPMs)

0

10000000

20000000

30000000

40000000

50000000

60000000

70000000

80000000

Jan-81 Oct-83 Jul-86 Apr-89 Dec-91 Sep-94 Jun-97 Mar-00 Dec-02

Month

RP

Ms

Difference of LogsDifference of Logs

-0.6

-0.4

-0.2

0.0

0.2

0.4

82 84 86 88 90 92 94 96 98 00 02

DLNREVENUE

Autocorrelation Partial 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.

Basic ObservationsBasic Observations

Clearly the series is highly regular, Clearly the series is highly regular, following a more or less constant cycle in following a more or less constant cycle in the difference of logs until September the difference of logs until September 2001. After that time there are distinctive 2001. After that time there are distinctive disturbances to that pattern.disturbances to that pattern.

Obviously September 11Obviously September 11thth was a major was a major factor, but we found two other significant factor, but we found two other significant disturbances.disturbances.

Coefficient Std. Error z-Statistic Prob. JAN -0.028213 0.009585 -2.943498 0.0032FEB -0.057530 0.008373 -6.871120 0.0000MAR 0.210592 0.008715 24.16362 0.0000APR -0.060710 0.012821 -4.735313 0.0000MAY 0.017059 0.012830 1.329640 0.1836JUN 0.085402 0.012386 6.895121 0.0000JUL 0.059078 0.011623 5.082747 0.0000AUG 0.034427 0.009840 3.498584 0.0005SEP -0.208309 0.013356 -15.59622 0.0000OCT 0.040941 0.015114 2.708797 0.0068NOV -0.077783 0.011069 -7.026996 0.0000DEC 0.037158 0.014168 2.622738 0.0087AR(1) -0.477291 0.077416 -6.165296 0.0000AR(2) 0.282609 0.086150 3.280437 0.0010SAR(12) 0.720921 0.082302 8.759497 0.0000MA(2) -0.717934 0.061278 -11.71602 0.0000SMA(12) -0.488249 0.095803 -5.096384 0.0000

Variance EquationC 0.000231 8.18E-05 2.823865 0.0047ARCH(1) 0.517236 0.161033 3.211984 0.0013GARCH(1) 0.199424 0.155043 1.286253 0.1984

Pre-9/11 RegressionPre-9/11 Regression

-0.15

-0.10

-0.05

0.00

0.05

0.10

-0.4

-0.2

0.0

0.2

0.4

84 86 88 90 92 94 96 98 00

Residual Actual Fitted

Autocorrelation Partial 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.

Autocorrelation Partial 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.

Residuals are fairly normal.Residuals are fairly normal.

0

5

10

15

20

25

30

-3 -2 -1 0 1 2 3

Series: Standardized ResidualsSample 1982:04 2001:08Observations 233

Mean -0.012509Median -0.011020Maximum 3.123842Minimum -3.165638Std. Dev. 1.001864Skewness 0.048184Kurtosis 3.626014

Jarque-Bera 3.894785Probability 0.142646

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

-0.6

-0.4

-0.2

0.0

0.2

0.4

01:01 01:07 02:01 02:07 03:01 03:07

DLNREVENUE HIGH LOW

Difference in recolored forecast Difference in recolored forecast versus actual (in 1000s of RPMs)versus actual (in 1000s of RPMs)

-5000

0

5000

10000

15000

20000

25000

01:01 01:07 02:01 02:07 03:01 03:07

DIFF

9/11

Troop deployment

War begins

Disruptions to the forecastDisruptions to the forecast

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

Coefficient Std. Error z-Statistic Prob. EVENT -0.354932 0.023675 -14.99178 0.0000AFTEREVENT 0.095437 0.013862 6.884513 0.0000PREEVENT2 0.094406 0.086043 1.097192 0.2726EVENT2 -0.258901 0.096246 -2.689993 0.0071AFTEREVENT2 0.142701 0.050712 2.813980 0.0049EVENT3 -0.238504 0.024301 -9.814738 0.0000AFTEREVENT3 0.246463 0.021573 11.42453 0.0000JAN -0.033452 0.009200 -3.636265 0.0003FEB -0.058835 0.007390 -7.961657 0.0000… … … … …NOV -0.081920 0.008804 -9.304688 0.0000DEC 0.051670 0.009952 5.192004 0.0000AR(1) -0.394049 0.068435 -5.758039 0.0000AR(2) 0.360461 0.091758 3.928399 0.0001SAR(12) 0.597508 0.106091 5.632041 0.0000MA(2) -0.745415 0.075285 -9.901220 0.0000SMA(12) -0.370395 0.115133 -3.217091 0.0013

Variance EquationC 0.000327 0.000128 2.548138 0.0108ARCH(1) 0.372624 0.133946 2.781891 0.0054GARCH(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.

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

-0.15

-0.10

-0.05

0.00

0.05

0.10

-0.6

-0.4

-0.2

0.0

0.2

0.4

84 86 88 90 92 94 96 98 00 02

Residual Actual Fitted

Residuals appear to be adequately clean.Residuals appear to be adequately clean.

Autocorrelation Partial CorrelationAC PAC Q-Stat Prob .|* | .|* | 1 0.073 0.073 1.3918 .|* | .|* | 2 0.081 0.076 3.1464 .|. | .|. | 3 -0.029 -0.041 3.3714 .|. | .|. | 4 0.029 0.028 3.5937 .|. | .|. | 5 0.028 0.030 3.8083 *|. | *|. | 6 -0.074 -0.085 5.2728 0.022 .|. | .|. | 7 0.032 0.041 5.5443 0.063 .|. | .|. | 8 -0.008 0.001 5.5599 0.135 *|. | *|. | 9 -0.078 -0.094 7.2197 0.125 *|. | *|. | 10 -0.078 -0.059 8.8698 0.114 .|* | .|* | 11 0.084 0.115 10.800 0.095 .|. | .|. | 12 0.020 0.001 10.911 0.143 .|. | .|. | 13 0.009 -0.006 10.933 0.206 .|* | .|* | 14 0.075 0.096 12.508 0.186 .|. | *|. | 15 -0.031 -0.061 12.769 0.237 .|. | .|. | 16 0.027 0.005 12.980 0.295 *|. | .|. | 17 -0.072 -0.037 14.423 0.275 .|. | .|. | 18 -0.003 -0.021 14.426 0.345 .|. | .|. | 19 0.000 -0.005 14.426 0.418 .|. | .|. | 20 0.025 0.054 14.600 0.481

Residuals Squared also clean.Residuals Squared also clean.Autocorrelation Partial CorrelationAC PAC Q-Stat Prob .|. | .|. | 1 0.024 0.024 0.1464 .|. | .|. | 2 -0.032 -0.033 0.4198 .|. | .|. | 3 -0.012 -0.011 0.4591 .|. | .|. | 4 -0.044 -0.045 0.9766 *|. | *|. | 5 -0.088 -0.087 3.0624 .|. | .|. | 6 0.021 0.022 3.1807 0.075 .|. | .|. | 7 -0.033 -0.041 3.4680 0.177 .|. | .|. | 8 -0.039 -0.040 3.8736 0.275 .|* | .|* | 9 0.075 0.068 5.4076 0.248 .|. | .|. | 10 -0.022 -0.035 5.5373 0.354 .|. | .|. | 11 -0.045 -0.040 6.0822 0.414 .|. | .|. | 12 0.019 0.011 6.1769 0.519 .|. | .|. | 13 -0.006 -0.010 6.1860 0.626 .|. | .|. | 14 -0.016 -0.005 6.2533 0.714 .|. | .|. | 15 -0.009 -0.023 6.2740 0.792 .|* | .|* | 16 0.088 0.088 8.4534 0.672 .|* | .|* | 17 0.108 0.113 11.707 0.469 .|. | .|. | 18 -0.033 -0.048 12.022 0.526 .|. | .|. | 19 -0.011 0.000 12.057 0.602 .|. | .|. | 20 -0.023 -0.010 12.213 0.663

Residuals are normal.Residuals are normal.

0

10

20

30

40

-3 -2 -1 0 1 2 3

Series: Standardized ResidualsSample 1982:04 2003:12Observations 261

Mean -0.020441Median -0.092000Maximum 2.900512Minimum -3.430903Std. Dev. 1.001946Skewness 0.035632Kurtosis 3.343782

Jarque-Bera 1.340502Probability 0.511580

The Pre-9/11 forecast and event model forecast give The Pre-9/11 forecast and event model forecast give almost identical results for 2004 growth rates. This almost identical results for 2004 growth rates. This

suggests that no further recovery from 9/11 is expected.suggests that no further recovery from 9/11 is expected.

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

04:01 04:03 04:05 04:07 04:09 04:11

PREEVENT_F DLNFUTURE

ConclusionConclusion

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

QUESTIONS?QUESTIONS?