Trends and patterns: how to find them and can you believe them?

1. Trends and patterns: how to find them and can you believe them? Michael Wood http://userweb.port.ac.uk/~woodm/mgtfut.ppt

2. My aim in this lecture is to • Give you an idea of the methods (especially statistical ones) that are used for analysing trends and making forecasts • Discuss some of the problems and limitations • Suggest some things to check for

3. Data and statistics Data are facts, figures and information You can either collect data yourself (primary data), or more likely get them from a book or website (secondary data) Statistics are things that can be worked out from data like averages, percentages, correlations, etc. (The word “statistics” also refers to techniques for analysing data.)

4. Data and statistics about the past Many sources – e.g. see unit guide and www.bized.co.uk (click on Data tab) www.statistics.gov.uk Usually the source will have a link to info about the meaning, derivation, limitations, etc. Read this! Always based on the past – may be yesterday, but more likely last month or last year … Often a reasonable understanding of the recent past is good enough, and likely to be all you can get. Can you get statistics about the future?

5. Are statistics always right? Inflation RPI (retail price index) inflation in Dec 09 was … 2.4% (http://www.statistics.gov.uk/cci/nugget.asp?ID=19) CPI (consumer price index) inflation was … 2.9% “Both measure the average change …in the prices of consumer goods and services purchased in the UK …” (http://www.statistics.gov.uk) … so can they both be right? Unemployment can be measured by counting benefit claimants or by a survey …Do you think the answers will be the same?

6. Three surveys to check accuracy of the NRE telephone service An NRE sponsored survey found that the answers were 97% correct A Consumer’s Association survey used a sample of 60 calls, mainly about fares. The worst mistake was when one caller asking for the cheapest fare from London to Manchester was told £162 instead of the cheaper £52 fare which was available via Sheffield and Chesterfield. The percentage correct was … 32% A reporter rang four times and each time asked for the cheapest route from London to Manchester. The proportion of the four answers which were correct was 25% (Source: Breakfast programme, BBC1 TV, April 30 2002.)

7. Things to check with data and statistics The sample – size and how selected. A random selection process usually best (e.g. Iraq death rate survey method, not adultery surveys in mags) Beware possible bias – e.g. “silent evidence” – Taleb (2008) How the statistic is defined (CPI vs RPI) If possible see if you can find alternative sources Remember that errors are almost inevitable – try to get an idea of how big the errors are likely to be Remember that there are always chance fluctuations – check statistical significance (see http://userweb.port.ac.uk/~woodm/stats/statnotes3.pdf )

8. Methods of forecasting Time series – look at the pattern over time of the thing you are trying to forecast Causal modelling – take account of other variables Simulations (e.g. of economy or global temperatures) – like a very detailed causal model Expert judgment Best to ask several experts independently – Delphi method Clairvoyance and time travel A real business opportunity!

9. Time series analysis • Plastic rulers • Why plastic? • Regression, moving averages, seasonal factors • See most books on business statistics • Many more advanced methods! • All assume that patterns in the past will continue • Be careful if this is not likely!

10. What would you predict for 2000?

11. Average surface temperature compared with 1961-90 average (Source: Hadley Centre)

12. Causal modelling to take account of other variables • Multiple regression • Very simple example at http://userweb.port.ac.uk/~woodm/stats/StatNotes4.pdf • More complex models • E.g. the prediction of the demand for long term care for elderly people in 2031 at http://www.statistics.gov.uk/CCI/article.asp?ID=1511&Pos=1&ColRank=1&Rank=224 • The structure of the model is explained on page 2. It involves dividing the population into categories … • A key finding is that residential and nursing home places need to increase by 65%. Do you believe this will turn out to be right?

13. Causal modelling to take account of other variables • Models can get very complicated and use advanced maths • This does not mean they are right • Common sense should give a clue about what cannot be forecast! • What factors are likely to be important for predicting the demand for long term care for elderly people in 2031? • Are these incorporated in the model?

14. Are forecasts always right? Different forecasters produce different results – e.g. see http://www.economicsuk.com/blog/000427.html No! They are almost never right. The question is how big is the error likely to be? Always consider the error in forecasts!

15. Things to check with forecasts Common sense Historical accuracy. How well did the methods do in the past? Measure by MAD, etc. Compare different forecasts Assumptions made (e.g. central assumptions) Probabilistic estimates may give a more realistic idea Likely impact of chaos …

16. Chaos • In theory, if we knew • Everything about how the world works, and • The exact state of affairs now We should be able to forecast the future accurately for ever? • Works for predicting positions of planets but not the weather, or human systems • Often small errors in (2) get magnified so predictions rapidly become useless – this is called chaos. • E.g. butterfly effect

17. Remember Statistics and forecasts are almost always less reliable than they may seem at first sight. Be careful!

18. When making or using forecasts of trends … • Remember the “Things to check …” slides • Remember that rare, and so unpredictable, events may have a massive impact. These have been called “black swans” by Taleb (2008). Life unpredictable and only appears explicable in retrospect. Statistics based on past often misleading! • Egs of black swans: the spread of the internet, the market crash of 1987, but not the present credit crunch which he says was predictable.

19. Reading • Gordon (2008) especially Chapter 7 (see Unit Guide) • Many books on standard statistical techniques. Also websites like http://www.statsoft.com/textbook if you want to check a particular technique • Taleb, N. (2008). The black swan: the impact of the highly improbable. London: Penguin. (Part 2 is entitled “We just can’t predict”. Econ and Stats as fraud.) • Ayres, I. (2008). Super crunchers: how anything can be predicted. London: John Murray. (Lots of examples of impressive predictions using regression models)