Guide to COST-BENEFIT ANALYSIS of investment projects - Ramiri

The method applied for the forecasting must be clearly explained and details on how the forecasts were preparedmay help in understanding the consistency and realism of forecasts.Interviewing expertsWhenever, for budget or time reasons, a quantitative methodology for demand forecasting cannot be applied,interviewing experts can provide independent external estimations of the expected impact of a project. Theadvantages of this approach are low cost and speed. Of course, this kind of estimation can be only qualitative or, ifquantitative, very approximate. Indeed, this approach can be recommended only for a very preliminary stage of theforecasting procedure.Trend extrapolationExtrapolation of past trends involves fitting a trend to data points from the past, usually with regression analysis.Various mathematical relationships are available that link time to the variable being forecasted (e.g. expecteddemand). The simplest assumption is a linear relationship, i.e.:where Y is the variable being forecasted and T is time.Another common model assumes constant growth rate, i.e.:Y= a + bTY= a(1+g) twhere Y is the variable being forecasted, a is a constant, g is the growth rate and t is time.The choice of the best model depends mainly on data. Whenever data is available for different times (e.g. years)statistical techniques can be used to find the best fitted model. When data is available only twice any model can befitted in principle (i.e. for each functional form parameters will always exist such as the two points lie on the curve).In such cases, additional information (e.g. trends observed in other contexts, different countries, etc.) should beused. Often, the Occam’s razor principle is applied: the simplest form is assumed unless specific informationsuggests a different choice. Therefore, a linear trend or a constant growth rate is applied in most cases.Extending an observed past trend is a commonly used approach, although one should be aware of its limitations.First, trend extrapolation does not explain demand, it just assumes that an observed past behaviour will continue inthe future. This may be quite a naïve assumption however. This is particularly true when new big projects are understudy; significant changes on the supply side can give rise to a break in past trends. Induced transport demand is acommon example.Multiple regression modelsIn the regression technique, forecasts are made on the basis of a linear relationship estimated between the forecast(or dependent) variable and the explanatory (or independent) variables. Different combinations of independentvariables can be tested with data, until an accurate forecasting equation is derived. The nature of the independentvariables depends on the specific variable to be forecasted.Some specific models have been developed to correlate demand to some relevant variables. For instance, theconsumption-level method considers the level of consumption, using standards and defined coefficients, and can beusefully adopted for consumer products. A major determinant of consumption level is consumer income,influencing, inter alia, the household budget allocations that consumers are willing to make for a given product. Withfew exceptions, product consumption levels demonstrate a high degree of positive correlation with the income levelsof consumers.Regression models are widely used and can have a strong forecasting power. The main drawbacks of this techniqueare the need for a large amount of data (as one should explore the role of several independent variables and, for eachone, a large set of values is required, across time or space) and the need for projections for the independent variables,which may be difficult. For instance, once we assume that consumption is income-dependent, the issue is then toforecast future income levels.A generalisation of the regression models is the econometric analysis where more sophisticated mathematical formsare used in which the variable being forecasted is determined by explanatory variables such as population, income,GDP, etc. As in the regression models, the coefficients are obtained from a statistical analysis and the forecastsdepend on projections of the explanatory variables.202