The use of time series analysis for the analysis of airlines
D.E.PitfieldTransport Studies Group
Department of Civil and Building EngineeringLoughborough University
LoughboroughLeicestershire LE11 3TU
UK
Paper presented at Fifth Israeli/British & Irish Regional Science Workshop, Ramat-Gan, Tel-Aviv, Israel, 29 April - 1 May 2007.
• Time Series Applications
– Oligopolistic Pricing of Low Cost Airlines• Cost Recovery?
– Impact of Ryanair on Market Share and Passenger Numbers
– Impact of Airline Alliances?• formation• Open skies agreements
Figure 1: A Location Map of Nottingham East Midlands Airport, UK.
Source: http://www.multimap.com/
DAY
29.0025.0021.0017.0013.009.005.001.00
£s 100
80
60
40
20
0
bmibaby fare
easyJet fare
Figure 3: Fares from EMA to Alicante
DAY
29.0025.0021.0017.0013.009.005.001.00
£s 100
80
60
40
20
0
bmibaby fare
easyJet fare
Figure 4: Fares from EMA to Malaga
Figure 15: Fares from LGW to Prague
DAY
49454137332925211713951
£s120
110
100
90
80
70
60
50
40
bmibaby fare
easyJet fare
Figure 7: CCF plot: Malaga
Lag Number
7531-1-3-5-7
CC
F -
bm
ibaby
and e
asyJ
et
1.0
.5
0.0
-.5
-1.0
Confidence L imits
Coefficient
ACF: bmibaby 0.899 easyJet 0.650
• ACF bmibaby 0.899 easyJet 0.650
CCF: 0.452 at lag 1day easyJet leading bmibaby
Figure 10: CCF plot: Alicante
Lag Number
7531-1-3-5-7
CC
F -
bm
ibaby
and e
asyJ
et
1.0
.5
0.0
-.5
-1.0
Confidence L imits
Coefficient
CCF: 0.808 at Lag 0
ACF:
bmibaby 0.375 easyJet 0.535
Figure 18: CCF plot. LGW-PRA
Lag Number
7531-1-3-5-7
CC
F -
bm
ibab
y a
nd e
asyJet
1.0
.5
0.0
-.5
-1.0
Confidence Limits
Coefficient
Figure 1: Ryanair’s Route Network
Figure 2: London Area Airports
Selected Airports
• Genoa
• Hamburg
• Pisa
• Stockholm
• Venice
London-Venice 1991-2003
JAN
1991A
UG
1991M
AR
1992O
CT
1992M
AY
1993D
EC
1993JU
L 1994F
EB
1995S
EP
1995A
PR
1996N
OV
1996JU
N 1997
JAN
1998A
UG
1998M
AR
1999O
CT
1999M
AY
2000D
EC
2000JU
L 2001F
EB
2002S
EP
2002A
PR
2003N
OV
2003
Date
0.00
10000.00
20000.00
30000.00
40000.00lgw
lhr
lcy
stntsf
stnvce
London-Venice 1991-2003
1999
18.5%
37.4%21.9%
22.2%LGW
LHR
LCY
STN-TSF
STN-VCE
2000
24.5%
25.7%28.6%
21.2%LGW
LHR
LCY
STN-TSF
STN-VCE
2001
45.5%
37.3%
17.2% LGW
LHR
LCY
STN-TSF
STN-VCE
2002
33.3%
45.6%
21.1%LGW
LHR
LCY
STN-TSF
STN-VCE
2003
30.8%
6.3%43.2%
19.6%LGW
LHR
LCY
STN-TSF
STN-VCE
Venice Intervention Model - with regular differencing
Parameters t tests Goodness of Fit
MA1 0.565 8.019 SE = 0.084
SAR1 -0.458 -5.981 Log Likelihood = 151.540
Intervention Ryanair
0.258 4.548 AIC = -295.081
Intervention GO
0.236 4.165 SBC = -283.229
RMS= 3156.129 U = 0.037 Um = 0.003, Us =0.001, Uc = 0.995
Minimum Start-Up Impact of Ryanair by destination
• Genoa – 44%
• Hamburg – 12%
• Pisa – 30%
• Stockholm – 10%
• Venice – 26%
Alliances• Oum et al (2000) Globalization and Strategic
Alliances: The Case of the Airline Industry
– Parallel Alliances
• Competition decreases
• Coordination of schedules
• Restricted output
• Increased fares
• FFPs
– Complementary Alliances
• Fares fall• Network Choices Improve• Traffic Falls?• Alliance Share increases?
Expectations and Perceptions
• Iatrou, K & Alamdari, F. (2005), The Empirical Analysis of the Impact of Alliances on Airline Operations, Journal of Air Transport Management
• Impact on traffic and shares is positive– hubs at O and D?– 1-2 years – Open skies has biggest impact
Data• North Atlantic – scale and role of alliances
• BTS T-100 International Market Data– monthly, January 1990- December 2003
• Hubs– Choice?
• European – LHR, CDG, FRA, AMS– not LHR or AMS
• USA – JFK, ORD, LAX
• Parallel
– CDG – JFK (Skyteam – AF and DL)– FRA – ORD ( Star Alliance – LH and UA)
• Complementary
– FRA – JFK ( Star Alliance – LH)– FRA – LAX (Star Alliance – LH/NZ)– CDG/ORY – BOS (Skyteam – AF)
ARIMA and Intervention Analysis
• Model traffic before Intervention(s)– Using parsimonious models
• Specify Intervention term and model whole data series– Abrupt impact– Gradual impact, over one or two years
• Exponential or stepped
– Lagged Abrupt impact
Figure 4.1: Traffic CDG-JFK 1990-2003
JAN
200
3
JAN
200
2
JAN
200
1
JAN
200
0
JAN
199
9
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199
8
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Figure 4.11: Alliance Share, CDG-JFK 1990-2003
JAN
200
3
JAN
200
2
JAN
200
1
JAN
200
0
JAN
199
9
JAN
199
8
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6
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5
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2
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199
0
Date
100.00
80.00
60.00
40.00
20.00
0.00
AF
shar
e
Paris (CDG) – New York (JFK)
A B C
Average monthly Average monthly Average monthly
traffic in the quarter traffic in the quarter traffic in the quarter
including start 1 year after A 2 years after A
of intervention
Traffic
Code sharing
42,573 54,529 58,128
Immunity
33,290 32,817 36,339
Alliance Share %
Code sharing
73.2 72.1 71.1
Immunity
77.9 77.4 75.8
• Seems? Traffic stimulated after code sharing and immunity. Shares?
• Intervention Analysis? – no significant intervention. Indigenous influences on traffic more important as well as other exogenous influences i.e. ceteris paribus
including 9/11 – 42% drop in total
Figure 4.2: Traffic CDG/ORY-BOS 1990-2003
JAN
200
3
JAN
200
2
JAN
200
1
JAN
200
0
JAN
199
9
JAN
199
8
JAN
199
7
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199
6
JAN
199
5
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4
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Figure 4.21: Alliance Share, CDG/ORY-BOS 1990-2003
JAN
200
3
JAN
200
2
JAN
200
1
JAN
200
0
JAN
199
9
JAN
199
8
JAN
199
7
JAN
199
6
JAN
199
5
JAN
199
4
JAN
199
3
JAN
199
2
JAN
199
1
JAN
199
0
Date
100.00
80.00
60.00
40.00
20.00
0.00
AF
shar
e
Paris (CDG/ORY) – Boston (BOS)
A B C
Average monthly Average monthly Average monthly
traffic in the quarter traffic in the quarter traffic in the quarter
including start 1 year after A 2 years after A
of intervention
Traffic
Code sharing
12,858 13,481 14,767
Immunity
10,434 8,924 10,004
Alliance Share %
Code sharing
47.2 61.7 69.8
Immunity
65.2 100.0 100.0
• Seems? Traffic increased from code sharing but not immediately from immunity. Shares? – AA!
• Intervention? Only nearly significant results are of a negative impact for traffic!
But this reflects 9/11 impact– Cannot model shares as partners have 0
traffic for some months
Figure 4.3: Traffic FRA-JFK 1990-2003
JAN
200
5
JAN
200
4
JAN
200
3
JAN
200
2
JAN
200
1
JAN
200
0
JAN
199
9
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199
8
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7
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6
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5
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Figure 4.31: Alliance Share, FRA-JFK 1990-2003
JAN
200
5
JAN
200
4
JAN
200
3
JAN
200
2
JAN
200
1
JAN
200
0
JAN
199
9
JAN
199
8
JAN
199
7
JAN
199
6
JAN
199
5
JAN
199
4
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199
3
JAN
199
2
JAN
199
1
JAN
199
0
Date
80.00
60.00
40.00
20.00
0.00
LH
UA
shar
e
Frankfurt(FRA) – New York(JFK)
A B C
Average monthly Average monthly Average monthly
traffic in the quarter traffic in the quarter traffic in the quarter
including start 1 year after A 2 years after A
of intervention
Traffic
Code sharing
42,064 42,856 43,090
Immunity
40,623 29,872 32,630
Alliance Share %
Code sharing
30.6 32.7 32.5
Immunity
33.0 46.5 51.7
• Seems? Little impact on traffic but impact on shares
• Intervention – not significant apart from a possible negative impact-contradicts expectations and theory of
complementary alliances
Figure 4.4: Traffic FRA-ORD 1990-2003
JAN
200
5
JAN
200
4
JAN
200
3
JAN
200
2
JAN
200
1
JAN
200
0
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199
9
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Figure 4.41: Alliance Share, FRA-ORD 1990-2003
JAN
200
5
JAN
200
4
JAN
200
3
JAN
200
2
JAN
200
1
JAN
200
0
JAN
199
9
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7
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6
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5
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4
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3
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2
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199
1
JAN
199
0
Date
90.00
80.00
70.00
60.00
50.00
LH
UA
shar
e
Frankfurt (FRA) – Chicago (ORD)
A B C
Average monthly Average monthly Average monthly
traffic in the quarter traffic in the quarter traffic in the quarter
including start 1 year after A 2 years after A
of intervention
Traffic
Code sharing
17,889 21,030 22,392
Immunity
22,392 23,632 32,472
Alliance Share %
Code sharing
73.1 74.5 76.8
Immunity
76.8 79.4 83.5
• Seems? Alliance partners hub at origin and destination so may expect a positive impact
• Traffic seems to increase especially from open skies. Shares up at both interventions
• Intervention. Results are positive and nearly significant contrary to theory of parallel alliances. Best results but not conclusive.
Figure 4.5: Traffic FRA-LAX 1990-2003
JAN
200
5
JAN
200
4
JAN
200
3
JAN
200
2
JAN
200
1
JAN
200
0
JAN
199
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Figure 4.51: Alliance Share, FRA-LAX 1990-2003
JAN
200
5
JAN
200
4
JAN
200
3
JAN
200
2
JAN
200
1
JAN
200
0
JAN
199
9
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199
8
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199
7
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199
6
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199
5
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199
4
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3
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199
2
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199
1
JAN
199
0
Date
100.00
90.00
80.00
70.00
60.00
50.00
40.00
LH
UA
shar
e
Frankfurt (FRA) – Los Angeles (LAX)
A B C
Average monthly Average monthly Average monthly
traffic in the quarter traffic in the quarter traffic in the quarter
including start 1 year after A 2 years after A
of intervention
Traffic
Code sharing
14,511 18,264 18,622
Immunity
18,622 19,319 17,134
Alliance Share %
Code sharing
51.1 54.4 51.4
Immunity
51.4 74.4 83.7
• Seems? Traffic stimulated from code sharing and shares up from open skies
• Intervention – no significant results. Major impact is probably the withdrawal of Continental some 11 months later and this causes alliance share to grow
Conclusion• Weak evidence suggests that impact of
complementary alliances is to reduce traffic and shares. Contrary to all theory.
• Some evidence that positive impact from parallel alliances when participants hub, but this is contrary to theory cf. expectations.
Generally, other things matter.
• Open Skies agreements appear to cause a decrease in traffic and competition; true for all alliance types – transatlantic traffic may not grow as these agreements spread.
• Alliance strength may be barrier to entry