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

Trends and patterns:
how to find them and can
you believe them?
Michael Wood
http://woodm.myweb.port.ac.uk/mgtfut.ppt
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
• I’ll start with statistics in general, then go
on to forecasting
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.)
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?
Are statistics always right?
•
Inflation
–
–
–
•
RPI (retail price index) inflation in Dec 2011 was …
4.8% (http://www.statistics.gov.uk/cci/nugget.asp?ID=19)
CPI (consumer price index) inflation was …
4.2%
“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?
Three surveys to check accuracy of
the NRE telephone service
1. An NRE sponsored survey found that the answers were
97% correct
2. 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%
3. 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.)
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 magazines)
– 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://woodm.myweb.port.ac.uk/stats/StatNotes3.pdf)
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!
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!
What would you predict for 2000?
Central England
0.5
0.0
-0.5
1943
1953
Average surface temperature compared with
1961-90 average (Source: Hadley Centre)
Central England
1.0
0.5
0.0
-0.5
-1.0
1862
1882
1902
1922
1942
1962
1982
2002
Causal modelling to take
account of other variables
• Multiple regression
– Very simple example at
http://woodm.myweb.port.ac.uk/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&Co
lRank=1&Rank=224 (accessed in Jan 2011)
– 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?
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?
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!
The recent financial crisis …
• Was predicted by almost nobody!
• Risks involved in some financial products
seriously underestimated …
– E.g. one model (Li, 2000) “took the current market traders’
assumptions about default probabilities, used an elegant, but
flawed, mathematical model to combine them and extrapolate
them into the future, and then fed these predictions back to the
traders. The result is that traders’ very fallible assumptions
seemed to be legitimized by the elegance of the mathematics.”
(Wood, 2012.)
Taleb (2008) and black swans
• Rare, and so unpredictable, events may have a
massive impact on the events. These have been
called “black swans” by Taleb (2008).
– 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.
• Life unpredictable and only appears explicable
in retrospect. Part 2 is entitled “We just can’t
predict”. Economics and Statistics as fraud!
Chaos
•
In theory, if we knew
1. Everything about how the world works, and
2. 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. Economic and social systems
may be chaotic.
–
E.g. butterfly effect
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
• Possible impact of black swans and chaos …
Remember
• Statistics and forecasts are almost always
less reliable than they may seem at first
sight.
• Remember the “Things to check …” slides
• Be careful!
References
• Many books on standard statistical techniques. Also websites like
http://www.statsoft.com/textbook if you want to check a particular
technique
• Ayres, I. (2008). Super crunchers: how anything can be predicted.
London: John Murray. (Lots of examples of impressive predictions
using regression models)
• Gordon (2008) especially Chapter 7 (see Unit Guide)
• Taleb, N. (2008). The black swan: the impact of the highly
improbable. London: Penguin.
• Wood, M. Why can’t measurements based on mathematical models
be more user-friendly? Problems, causes and suggestions. Draft at
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1983228