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The use of time series analysis for the analysis of airlines

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

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The use of time series analysis for the analysis of airlines

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  1. 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 Paper presented at Fifth Israeli/British & Irish Regional Science Workshop, Ramat-Gan, Tel-Aviv, Israel, 29 April - 1 May 2007.

  2. 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

  3. Figure 1: A Location Map of Nottingham East Midlands Airport, UK. Source: http://www.multimap.com/

  4. Figure 3: Fares from EMA to Alicante

  5. Figure 4: Fares from EMA to Malaga

  6. Figure 15: Fares from LGW to Prague

  7. Figure 7: CCF plot: Malaga

  8. ACF: bmibaby 0.899 easyJet 0.650 ACF bmibaby 0.899 easyJet 0.650 CCF: 0.452 at lag 1day easyJet leading bmibaby

  9. Figure 10: CCF plot: Alicante

  10. CCF: 0.808 at Lag 0 ACF: bmibaby 0.375 easyJet 0.535

  11. Figure 18: CCF plot. LGW-PRA

  12. Figure 1: Ryanair’s Route Network

  13. Figure 2: London Area Airports

  14. Selected Airports • Genoa • Hamburg • Pisa • Stockholm • Venice

  15. London-Venice 1991-2003

  16. London-Venice 1991-2003

  17. 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

  18. Minimum Start-Up Impact of Ryanair by destination • Genoa – 44% • Hamburg – 12% • Pisa – 30% • Stockholm – 10% • Venice – 26%

  19. 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

  20. Complementary Alliances • Fares fall • Network Choices Improve • Traffic Falls? • Alliance Share increases?

  21. 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

  22. 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

  23. 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)

  24. 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

  25. Figure 4.1: Traffic CDG-JFK 1990-2003

  26. Figure 4.11: Alliance Share, CDG-JFK 1990-2003

  27. 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

  28. 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

  29. Figure 4.2: Traffic CDG/ORY-BOS 1990-2003

  30. Figure 4.21: Alliance Share, CDG/ORY-BOS 1990-2003

  31. 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

  32. 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

  33. Figure 4.3: Traffic FRA-JFK 1990-2003

  34. Figure 4.31: Alliance Share, FRA-JFK 1990-2003

  35. 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

  36. 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

  37. Figure 4.4: Traffic FRA-ORD 1990-2003

  38. Figure 4.41: Alliance Share, FRA-ORD 1990-2003

  39. 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

  40. 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.

  41. Figure 4.5: Traffic FRA-LAX 1990-2003

  42. Figure 4.51: Alliance Share, FRA-LAX 1990-2003

  43. 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

  44. 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

  45. 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.

  46. 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

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