the use of time series analysis for the analysis of airlines
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The use of time series analysis for the analysis of airlines. D.E.Pitfield Transport Studies Group Department of Civil and Building Engineering Loughborough University Loughborough Leicestershire LE11 3TU UK - PowerPoint PPT PresentationTRANSCRIPT
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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.
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• 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
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Figure 1: A Location Map of Nottingham East Midlands Airport, UK.
Source: http://www.multimap.com/
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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
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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
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Figure 15: Fares from LGW to Prague
DAY
49454137332925211713951
£s120
110
100
90
80
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60
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bmibaby fare
easyJet fare
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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
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ACF: bmibaby 0.899 easyJet 0.650
• ACF bmibaby 0.899 easyJet 0.650
CCF: 0.452 at lag 1day easyJet leading bmibaby
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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
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CCF: 0.808 at Lag 0
ACF:
bmibaby 0.375 easyJet 0.535
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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
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Figure 1: Ryanair’s Route Network
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Figure 2: London Area Airports
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Selected Airports
• Genoa
• Hamburg
• Pisa
• Stockholm
• Venice
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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
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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
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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
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Minimum Start-Up Impact of Ryanair by destination
• Genoa – 44%
• Hamburg – 12%
• Pisa – 30%
• Stockholm – 10%
• Venice – 26%
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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
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– Complementary Alliances
• Fares fall• Network Choices Improve• Traffic Falls?• Alliance Share increases?
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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
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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
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• 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)
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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
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Figure 4.1: Traffic CDG-JFK 1990-2003
JAN
200
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Figure 4.11: Alliance Share, CDG-JFK 1990-2003
JAN
200
3
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AF
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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
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• 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
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Figure 4.2: Traffic CDG/ORY-BOS 1990-2003
JAN
200
3
JAN
200
2
JAN
200
1
JAN
200
0
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199
9
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199
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Figure 4.21: Alliance Share, CDG/ORY-BOS 1990-2003
JAN
200
3
JAN
200
2
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200
1
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200
0
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199
9
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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
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• 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
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Figure 4.3: Traffic FRA-JFK 1990-2003
JAN
200
5
JAN
200
4
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3
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2
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Figure 4.31: Alliance Share, FRA-JFK 1990-2003
JAN
200
5
JAN
200
4
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200
3
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80.00
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LH
UA
shar
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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
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• 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
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Figure 4.4: Traffic FRA-ORD 1990-2003
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200
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Figure 4.41: Alliance Share, FRA-ORD 1990-2003
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200
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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
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• 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.
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Figure 4.5: Traffic FRA-LAX 1990-2003
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200
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Figure 4.51: Alliance Share, FRA-LAX 1990-2003
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200
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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
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• 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
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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.
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• 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