2011 02-04 - d sallier - méthode kenza


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2011 02-04 - d sallier - méthode kenza

  1. 1. Long-term Demand Forecast:The Kenza ApproachPrepared & presented by Daniel SALLIERAir Traffic Data & Forecasting DirectorPorto, ATRS 2010
  2. 2. 2 Content A. Forewords C. The 1st Kenza law of consumer demand - an empirical approach E. Long term demand elasticity change G. Kenza approach vs more classical ones I. Discussion & conclusion Paris, ENGREF 4 février 2011
  3. 3. 3 Forewords Paris, ENGREF 4 février 2011
  4. 4. Air Transport, an industry with strong expectations for4 accurate (very) long-term forecast With the noticeable exception of the airlines for which a 3 to 5 years ahead market visibility is far sufficient, most of the other actors of this industry need a 15 to 20 years visibility: – Aircraft & engine manufacturers: further to project development lead times (10 years a very minimum) and long production times before any possible return on investment; – Airports & ATCs: same reasons as for the aircraft and engine manufacturers; – Credit export organisations & bankers: risk assessment on residual values (for aircraft type and portfolio of aircraft); – Civil Aviation Authorities & Civil Aviation related organisations (i.e. ICAO, IATA, ACI, European Commission, etc.): rules enactment, market regulation, state support, infrastructure financing. Paris, ENGREF 4 février 2011
  5. 5. General methodological background and related5 drawbacks Most of the demand/traffic models worldwide and industry-wide are based on econometrical models. Econometrical modellers are faced with 3 conditions they can hardly meet for long-term forecasting … most of the time: Twice as long rule of the thumb. Time series should be about twice as long as the forecasting horizon: 40 years times series for a 20 years ahead forecast. Steady behaviour of the consumers over the time. Further to the social and economical behaviour inertia of any population it is a valid assumption to be done for short and mid term forecast (up to 5 years ahead). It is a far more dubious assumption on longer term issues. “Clean” time series. Unfortunately the probability is pretty great that events (epidemics, wars, terrorist attacks, paranoid government, etc.) “contaminate” the available historical data translating into a behaviour (transient) change of the consumer habits which demands the corresponding data to be disregarded (outliers). Paris, ENGREF 4 février 2011
  6. 6. 6 Twice as long rule: The US domestic market case, one of the best cases of data availability in the world! Both 8 demand models demonstrate excellent calibration characteristics over the same common 1982-1990 period of time which cannot allow us to identify the best one(s) among them. A backward traffic forecast over the 1991-2009 period of time allows us to identify: the linear model in real US dollars, GDP, price & population based, as the best candidate for providing rather accurate 10 years ahead forecast (1991-2000). Most of the models for short/mid term 5 years ahead forecast (up to 1996) GDP/capita based models do not provide good forecasting models even for short and medium term forecasting. Paris, ENGREF 4 février 2011
  7. 7. 7 “Clean” dataset issue: The US domestic market case, one of the best cases of data availability in the world! A. 1975-2009: 34 years of traffic history and 35 annual data available. C. 1975-1980: pre-deregulation period: 6 years of un-usable traffic data. E. 1991-1994: Gulf War the first and its aftermaths: 4 years of un-usable traffic data. G. 2001-2004: Sept. 11 attacks + Afghanistan war + Gulf War the second + SRAS: 4 years of un-usable traffic data. I. 2008…: The sub-prime crisis turning into a worldwide financial crisis, turning into a severe economical recession: 2 years of hardly-usable traffic data. 19 “usable” traffic data left over a total of 35. Paris, ENGREF 4 février 2011
  8. 8. Behavioural modelling: a way to provide the information8 missing in time series Most of the time the 3 prerequisites: Twice as long rule of the thumb. Steady behaviour of the consumers over the time. “Clean” time series. … for a fully “orthodox” use of econometric models cannot be met for long- term forecasting. But, isn’t better to get something than having nothing to play with? A behavioural approach which models consumers’ behaviour should turn out to be much less “data-thirsty” since part of the missing information in the time series for a “classical” approach is provided by the behaviour modelling. Paris, ENGREF 4 février 2011
  9. 9. Behavioural modelling: a way to provide the missing9 information in time series (continued) t1 t 2 The comet later 2 months appears in the sky First measurements Latest measurements Newton law of motion… t3 Anticipation of the M1 ⋅ M 2 F =C⋅ comet position using “classical” approach t3 R2 … is not and has never been Anticipation of the comet position using a matter of correlations a “behavioural” approach: Newton law of motion. between F, M1, M2 and R Paris, ENGREF 4 février 2011
  10. 10. 10 The 1st Kenza law of consumer demand; an empirical approach Paris, ENGREF 4 février 2011
  11. 11. Why people are flying?11 3 very basic ideas for a very simple question People who pay for their ticket are flying because: • They feel like or they have too; • They can afford it; • They are sensitive to the relative ticket/inclusive tour price to their revenue; People flying with a company paid ticket belongs to those among the better paid in the company. It simply means that annual individual income could be a good indicator of their ability to be paid business trips. Paris, ENGREF 4 février 2011
  12. 12. Demand formation12 1st formulation A. Ticket (inclusive tour) price should determine a threshold of minimum income; C. Given the distribution of income and the threshold of income it is possible to determine the part of the population which can afford to fly: the elected population; E. Only part of the elected population actually buys a ticket (inclusive tour): actual customers; G. Each customer buys an average number of flights which sum up in the total demand. Paris, ENGREF 4 février 2011
  13. 13. Demand formation13 2nd formulation; data normalisation Price & population normalisation: Instead of using actual monetary values, we use price/revenue related to a normalisation quantity such as the average revenue per capita or the GDP per capita. Instead of using population related cumulative distribution of income, we use % of total population related cumulative distribution of normalised income: so called Kenza distribution; D. Advantage #1: no inflation bias in the model (a normalised price or revenue keeps the same if computed out of real or current values), no currency exchange rate bias; F. Advantage #2: Kenza distribution very steady over the time which is very convenient for long-term forecasting. Paris, ENGREF 4 février 2011
  14. 14. Kenza distributions14 data source As a general trend, 3 sources for household income data exist: – Census organisations such as the US Census Bureau. – National statistics administrations such as DESTATIS in Germany, INSEE in France, Statistiques Canada in Canda, etc.; – Specific organisations such as Banca d’Italia in Italy or DWP (Department of Work and Pension) in UK. Most commonly 2 formats of data: – Double-entry tables which cross income categories and household categories such as the one disseminated by the US Census Bureau; – Individual data tables which provide household characteristics such as income, number of members, etc. for each household of the sample. 100% of the time household income data result from a population survey Paris, ENGREF 4 février 2011
  15. 15. Kenza distributions15 Pretty steady over the time; the US case The US Kenza distributions prove to be very steady over the time while, at the same time, US household structure and income could have significantly changed: steadiness is a macro phenomenon it is not a micro one; The US case is very far from being unique. (i.e.: Italy, San salvador, even Poland); It is a very convenient characteristic for long term forecasting! Paris, ENGREF 4 février 2011
  16. 16. 16 1st formulation of the Kenza law of demand D = P ⋅ K1,1 ⋅ K1, 2 ⋅ F ( K 2 ⋅ pn ) * Paris, ENGREF 4 février 2011
  17. 17. 2nd formulation of the Kenza law of demand17 The “percolation” principle D = P ⋅ K1,1 ⋅ K1, 2 ⋅ F * ( K 2 ⋅ pn ) D ( t ) = P ( t ) ⋅ K1 ⋅ F * ( K 2 ⋅ pn ( t ) ) We assume that K1,1, K1,2 and K2 are constant over the time, but differ from one set of populations to another. K1,1 and K1,2 are concatenated into a single constant K1 Such an assumption is equivalent of assessing that consumer behaviour is fully characteristic of a given population and not likely to change over the time or, stated a different way, that people tend to adopt the same consumption habits as those of their “adjacent” upper social class as soon as they join it. It is a “percolation” social behaviour proposed and detailed by James Stemble DUESENBERRY in "Income, Saving and the Theory of Consumer Behavior" Harvard University Press – January 1949. Paris, ENGREF 4 février 2011
  18. 18. 18 Some examples of very steady phenomenon over the time The relationship between Paris (air) traffic and the French GDP (same between US domestic air traffic and US GDP) Kenza distribution of income in the USA Ground transportation: Andreas SCHÄFER and David G. VICTOR, 2000, in "The Future Mobility of the World Population", Transportation Research A, Volume 34, issue 3: pp 171-205 : "On average a person spends 1.10 h per day traveling and devotes a predictable fraction of income to travel. We show that these time and money budgets are stable over space and time and can be used for projecting future levels of mobility and transport mode.” Paris, ENGREF 4 février 2011
  19. 19. 19 US domestic market as a test bench The US domestic (air) market is the best documented traffic in the world which makes it a “natural” test bench. The linear regression between the actual traffic data and its Kenza estimate over the uneventful periods 1981-1990, 1994-2000 and 2005-2008 results in a R²=0.975. It looks like the Kenza approach delivers what it has been designed for: ability to provide long term forecast out of a limited number of historical data. Paris, ENGREF 4 février 2011
  20. 20. 20 Long term demand elasticity change Paris, ENGREF 4 février 2011
  21. 21. 21 Kenza intrinsic elasticity Let us define Kenza intrinsic elasticity of a Kenza distribution as: dF ( rn ) rn * ε K ( rn ) = * ⋅ ≤0 F ( rn ) drn This quantity is related to the Kenza distribution shape out of any direct considerations on demand functions. It can be proved that the intrinsic elasticity is completely independent of the normalisation quantity which is used (i.e. GDP/capita, average individual income, median individual income, etc...) and, thus, is fully characteristic of the population which is studied. The demand elasticity to price is equal to: ε p = ε K ( K 2 ⋅ pn ) Paris, ENGREF 4 février 2011
  22. 22. Kenza intrinsic elasticity as a function of the elected22 population: the long term market maturation process Analysis of actual Kenza distributions of several countries, such as the USA, shows quite large sections of an almost linear relationship between the intrinsic elasticity and the corresponding elected population (sorted by decreasing level of income). The 1st Kenza law of demand takes "naturally" into account the long term market rather linear maturation process (demand elasticity to price decreasing in absolute value). The market maturation process is not related to the market which is studied. It means, in our example, that it has nothing to do with the US domestic air market itself. It exclusively results from the way individual incomes are distributed with the considered population . The only phenomenon which is market related is how fast the elected population grows or decreases over the time . Paris, ENGREF 4 février 2011
  23. 23. 23 Kenza approach vs more classical ones Paris, ENGREF 4 février 2011
  24. 24. 24 Kenza vs econometrical models 8 econometrical models are considered which are detailed hereafter: All the models, Kenza model included, are calibrated on the same 1982-1990 period of the US domestic (air) traffic. Paris, ENGREF 4 février 2011
  25. 25. 25 US domestic market for methodological benchmarking All the models present excellent calibration characteristics Paris, ENGREF 4 février 2011
  26. 26. US domestic market for methodological benchmarking26 (continued) GDP/capita based models do not provide any adequate forecasting accuracy even for short and mid-term purposes. The Kenza model and the linear one based on GDP, and prices stated in real dollar provide a good 10 years ahead forecast. Only the Kenza model proves decent accuracy characteristics up to 18 years ahead (within the frame of this example, of course). Paris, ENGREF 4 février 2011
  27. 27. US domestic market for methodological benchmarking27 model parameters stability A behavioural model should be rather insensitive to the selected calibration period. That is exactly what the Kenza model proves to be, at least within the frame of this example. Paris, ENGREF 4 février 2011
  28. 28. US domestic market for methodological benchmarking28 the “added” value of the Kenza distribution The simplified Kenza model is built considering a linear section of the Kenza distribution over the calibration period, linear section which is extrapolated in the future. The difference between the Kenza model and the simplified Kenza model provide the added value, mostly for long term purposes, of using the Kenza distribution; a non linear effect. Paris, ENGREF 4 février 2011
  29. 29. 29 Discussion & conclusion Paris, ENGREF 4 février 2011
  30. 30. 30 Is it a “mathematical freak”? … A. A Kenza model is based on the use of a S-shaped curve – the Kenza distribution – which can be stretched/shrunk horizontally – the K2 parameter – for adjusting the trend slope and which can be stretched/shrunk vertically – the K1 parameter – for adjusting the absolute volume. The probability is high, whatever the S-curve selected, to find a couple (K1 , K2) parameters which allows a fine “tuning” of a Kenza estimate over the actual historical data; K2 effect K1 effect Paris, ENGREF 4 février 2011
  31. 31. … or does it sustain its claim of being a behavioural31 approach? The S-curve approach can explain the short and mid- term forecasting capabilities which are confirmed by the simplified Kenza approach; But it cannot explain the long-term forecasting capability since the calibration process does not take into account future non linear corrections introduced by the Kenza distribution… So, we keep claiming it is a behavioural approach and not a “mathematical freak”. Paris, ENGREF 4 février 2011
  32. 32. 32 The very “disturbing” K1 and K2 being constant assumption A. Assuming K1 and K2 constant over the time is quite a strong assumption to do. C. Examples of very steady phenomenon over very long period of time can be quoted and have been quoted (i.e. ground transportation time, air traffic vs GDP, etc.). It just proves that it is possible, it does not prove that it is the case. E. Long-term forecasting capability of a Kenza model should be considered as an indirect clue only. G. Additional research works, yet to be published, based on the aggregation of individual probabilistic consuming behaviour, fully sustain the K1 and K2 being constant over the time assumption. Paris, ENGREF 4 février 2011
  33. 33. Conclusions33 Kenza modelling of consumer demand proves: to provide rather accurate long-term forecast which is illustrated in the case of the US domestic air market; to be a pretty robust model of which the low sensitivity of the parameters to changes in the calibration period could be considered as an indirect confirmation of the "behavioural" nature of its modelling. And, as a side product, it gives access to the demographic, social and economical background of a market which proves to be a valuable asset mostly when presenting the outputs to a top management meeting. It is far more comfortable coming with explanations than correlations!; To provide an unanticipated ability at the time of the method development to provide additional valuable pieces of information such as the intrinsic elasticity and how it directly drives the long term evolution of demand elasticities and may provide clues on the market maturation process. To be a more general approach than just modelling air demand and can be extended to consumer demand issues. Kenza approach was initially developed for the needs of the Marketing Directorate of Airbus Industrie and has been the primary annual demand forecasting tool of Aéroports de Paris since 2003. Paris, ENGREF 4 février 2011
  34. 34. 34 What’s Kenza for? Thank you It’s my daughter’s name, for … this kind of models are like children, you have to take care of them all your life long! Paris, ENGREF 4 février 2011