Strategy Survival Guide

Strategy Survival Guide Version 2.1
Prime Minister’s Strategy Unit
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Strategy Skills > Building an Evidence Base
Looking forward - Forecasting
> in practice
Forecasting identifies and tracks past trends and extrapolates them into the future. Typically, it is used to
track over time (time-series forecasting), and to make predictions about differences among people, firms or
other objects (cross-sectional forecasting). As well as quantitative (statistical methods), it also includes the
use of more qualitative (judgmental) methods.
As looking into the future naturally involves a varying degree of uncertainty, sometimes a distinction is drawn
between forecasting and projections. In certain contexts, particularly economic ones, forecasting is used to
refer to short-term extrapolations associated with a reasonable degree of certainty. Projections are
considered to be longer-term, more sophisticated, but also less reliable. This distinction does not always hold
true, for example demographic projections can be very reliable over the time span of a generation. For this
reason this section distinguishes instead between quantitative and qualitative trend analysis.
Quantitative Analysis
Quantitative trend analysis is probably the most common forecasting method. It relies on the statistical
analysis of historical data – in other words it is relatively objective. Quantitative techniques include
extrapolation (such as moving averages, linear projections against time or exponential smoothing) and
econometric methods (typically using regression techniques to estimate the effects of causal variables). This
type of analysis is commonly used to forecast demographic and economic changes where extrapolating over
time is believed to have some validity.
The Strategy Unit, A Futurist’s Toolbox, identifies some of the main quantitative techniques used by
forecasters. Other techniques for short to medium term analysis and forecasting include:
Modelling
Modelling is an extremely useful tool for quantitative analysis. Excel and econometric modelling techniques
are outlined in the modelling section of the guide.
Simple Moving Averages
The best-known forecasting method is moving averages. It simply takes a certain number of past periods
and adds them together, then divide by the number of periods. Simple Moving Averages (MA) is an effective
and efficient provided the time series is stationary in both mean and variance. The following formula is used
in finding the moving average of order n, MA(n) for a period t+1,
MA t+1 = [D t + D t-1 + ... +D t-n+1 ] / n
where n is the number of observations used in the calculation.
The forecast for time period t+ 1 is the forecast for all future time periods. However, this forecast is revised
only when new data becomes available.
Weighted Moving Averages
Very powerful and economical. They are widely used where repeated forecasts required-uses methods like
sum-of-the-digits and trend adjustment methods. As an example, a Weighted Moving Averages is:
Weighted MA(3) = w 1 .D t + w 2 .D t-1 + w 3 .D t-2
where the weights are any positive numbers such that: w1 + w2 + w3 =1. A typical weights for this example
is, w 1 = 3/(1 + 2 + 3) = 3/6, w 2 = 2/6, and w 3 = 1/6.
Strategy Survival Guide – Strategy Skills
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