Financial forecasting is an important tool for appraising public sector strate- gies and an effective financial forecast allows for improved strategic decision making. However, the forecast should not stand alone. The future is uncertain and so future financial forecasts will also be uncertain. The underlying assump- tions and methodology behind the forecasts should be clearly stated and made available to stakeholders so that they can judge what the forecasts are actually saying. Also, the forecast should be regularly monitored and periodically up- dated in the light of changing circumstances.
Forecasting is not just about spreadsheets and statistical methods. A fore- casting project requires a framework which is illustrated in Figure 9.4.
Forecasting process
In undertaking financial forecasting in public services, there are a wide range of different techniques which can be utilised. But these techniques should not
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be considered in isolation but must be integrated into a formal management process which considers matters such as what forecasts are to be made, how they are to be made, who will be involved and how the results are to be used.
In relation to forecasting, the key elements of a forecasting process can be sum- marised below:
• Involvement – who is to be involved in the forecasting exercise? Clearly there will need to be technical experts used to working with the various forecasting techniques but there are others.
• Approach – the approach to the forecasting exercise needs to be clearly defined and the various tasks identified. The type of forecasting methods to be used need to be established and the process of interpreting and acting on the results needs to be clarified.
• Responsibilities – t he forecasting process can be quite a complex exercise with various stages concerning design, data collection, inter- pretation etc. It is important to have a proper project plan and be clear about who is doing what. The importance of this cannot be over- emphasised.
• Consultation – as well as establishing a core project team for the exercise, it is important to consult a wider audience to gain knowledge about the area of activity being considered. Thus, consultation arrangements need to be clarified.
• Reporting – it needs to be clear how the results of the forecasting exercise are to be presented and to whom they should be reported. Also, the struc- ture of the report on the exercise should be considered.
FORECASTING PROJECT
e.g.
• future service demand,
• future costs
Forecasting Process Forecasting
Guidelines
Forecasting Stages
Forecasting Methods/Techniques FIGURE 9.4
Structure of a forecasting project.
Forecasting guidelines
There are nine overarching principles ( Armstrong et al. 2010) that can help to improve forecasting accuracy and the relevance of the forecasts to the tasks in hand.
1. Match the forecasting method to the situation – conditions for forecasting vary. No single best method works for all situations. Consider the meth- ods available and select the most appropriate based on evidence available.
Interestingly, generalisations based on empirical evidence sometimes con- flict with common beliefs about which forecasting method is best.
2. Use existing domain knowledge – managers and analysts typically have useful knowledge about situations. While this domain knowledge can be important for forecasting, it is often ignored. Managers’ expectations are particularly important when their knowledge about the direction of the trend in a time series conflicts with historical trends in the data. If one ignores existing domain knowledge about contrary series, large errors are likely.
3. Structure the problem – one of the basic strategies in management research is to break a problem into manageable pieces, solve each piece, then put them back together. This decomposition strategy is effective for forecasting, especially when there is more knowledge about the pieces than about the whole. Decomposition is particularly useful when the forecast- ing task involves extreme ( very large or very small) numbers. When the components of the series can be forecasted more accurately than the global series, using causal forces to decompose the problem increases forecasting accuracy.
4. Model the experts’ forecasts – expert systems represent forecasts made by experts and can reduce the costs of repetitive forecasts while improving accuracy. However, expert systems are expensive to develop. An inexpen- sive alternative to expert systems is termed judgmental bootstrapping. It translates an experts’ rules into a quantitative model by regressing the experts’ forecasts against the information that was used. Bootstrapping models apply an experts’ rules consistently, and studies have shown that decisions and predictions from bootstrapping models are similar to those from the experts.
5. Represent the problem realistically – this has been discussed earlier. It is common practice to start with an existing model and attempt to generalise to the situation. Instead, one should start with the situation and develop a realistic representation in the model. Realistic representations are espe- cially important when forecasts based on unaided judgment fail.
6. Use causal models when you have good information – an important advantage of causal models is that they reveal the effects of alternative decisions on the outcome. However, this means that the forecaster must understand the factors that have an influence on the variable to forecast and possesses enough data to estimate a regression model. To satisfy the first condition, the analyst can obtain knowledge about the situation from domain knowledge and from prior research.
CHAPTER 9 Public service strategy 165 7. Use simple quantitative methods – there is a danger of automatically
preferring complexity over simplicity. Complex models are often misled by noise in the data, especially in uncertain situations. Thus, using simple methods is important when there is much uncertainty about the situation.
8. Be conservative when uncertain – one should make conservative forecasts for uncertain situations. If historical trends are subject to variations, dis- continuities and reversals, one should be cautious with extrapolating the historical trend. Only when historical time series show a long steady trend with little variation, should one extrapolate the trend into the future.
9. Combine forecasts – combining is especially effective when different forecasting methods are available. One could use as many as five different methods, and combine their forecasts using a predetermined mechanical rule. Lacking strong evidence that some methods are more accurate than others, one could use a simple average of forecasts.