The use of time series analysis for the

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 LE 11 3 TU 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/

Figure 3: Fares from EMA to Alicante

Figure 4: Fares from EMA to Malaga

Figure 15: Fares from LGW to Prague

Figure 7: CCF plot: Malaga

CCF: 0. 452 at lag 1 day easy. Jet leading bmibaby ACF: bmibaby 0. 899 easy. Jet 0. 650

Figure 10: CCF plot: Alicante

CCF: 0. 808 at Lag 0 ACF: bmibaby 0. 375 easy. Jet 0. 535

Figure 18: CCF plot. LGW-PRA

Figure 1: Ryanair’s Route Network

Figure 2: London Area Airports

Selected Airports • • • Genoa Hamburg Pisa Stockholm Venice

London-Venice 1991 -2003

London-Venice 1991 -2003

Venice Intervention Model - with regular differencing Parameters t tests Goodness of Fit MA 1 0. 565 8. 019 SE = 0. 084 SAR 1 -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

Figure 4. 11: Alliance Share, CDGJFK 1990 -2003

Paris (CDG) – New York (JFK) A B C Average monthly 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 Immunity 77. 9 77. 4 71. 1 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

Figure 4. 21: Alliance Share, CDG/ORY-BOS 1990 -2003

Paris (CDG/ORY) – Boston (BOS) A B C Average monthly 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

• 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

Figure 4. 31: Alliance Share, FRA-JFK 1990 -2003

Frankfurt(FRA) – New York(JFK) A B C Average monthly 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

Figure 4. 41: Alliance Share, FRA-ORD 1990 -2003

Frankfurt (FRA) – Chicago (ORD) A B C Average monthly 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 19902003

Figure 4. 51: Alliance Share, FRALAX 1990 -2003

Frankfurt (FRA) – Los Angeles (LAX) A B C Average monthly 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 alliance types – transatlantic traffic may not grow as these agreements spread. • Alliance strength may be barrier to entry