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4. Marcos de cooperación con respecto al río Nilo
Certain conditions should be met by any good forecast:
➤ A forecast must be consistent with other parts of the business. For instance, a sales forecast of 10 percent growth must ensure there are sufficient manufacturing facili-ties and labor force to produce this increase.
➤ A forecast should be based on knowledge of the relevant past. However, when un-derlying conditions have changed significantly, past experience may be of no help in making a forecast. Moreover, sometimes there is no past on which to rely. This is the case when we are dealing with a new product or technology. Under these circumstances, analysts’ judgments must be injected into the forecasting process. In some cases, “forecasts based purely on the opinion of ‘experts’ are used to formu-late the forecast or scenario for the future.”17
➤ A forecast must consider the economic and political environment, as well as any potential changes.
➤ A forecast must be timely. An accurate forecast that is too late to be acted on may be worthless.
17John E. Hanke, Dean W. Wichern, and Arthur G. Reitsch, Business Forecasting, 7th ed., Upper Saddle River, NJ: Prentice Hall, 2001, p. 421. This is another text we recommend for students who are interested in exploring the subject of forecasting in more detail.
FOrECAStInG tEChnIquES
There are many different forecasting methods. One of the challenges facing a fore-caster is choosing the right technique. The appropriate method depends on the sub-ject matter to be forecast and on the forecaster, but we can discuss some of the factors that enter into consideration.18
18This section relies heavily on Wheelwright and Makridakis, Forecasting Methods, pp. 30–31.
1. The item to be forecast. Is one trying to predict the continuance of a historical pattern, the continuance of a basic relationship, or a turning point?
2. The interaction of the situation with the characteristics of available forecasting methods.
The manager must judge the relation between value and cost. If a less expensive method can be used to achieve the desired results, it certainly should be.
3. The amount of historical data available.
4. The time allowed to prepare the forecast. Selection of a specific method may depend on the urgency of the situation.
One other point about forecasting cost and accuracy should be added here.
Generally, when the requirements for forecast accuracy are high, more sophisticated and more complex methods may be used. Such methods are, as a rule, more costly.
Thus, a manager will authorize greater expenditures when relatively high accuracy is warranted. However, “[e]mpirical studies have shown that simplicity in forecast-ing methods is not necessarily a negative characteristic or a detriment with regard to forecasting accuracy. Therefore, the authors would advise against discarding simple methods and moving too quickly to replace them with more complex ones.”19
Forecasting techniques can be categorized in many ways. We use the following six categories:
1. Expert opinion
2. Opinion polls and market research 3. Surveys of spending plans
4. Economic indicators 5. Projections
6. Econometric models
As we see in the following pages, some of the methods can be classified as quali-tative, others as quantitative. Qualitative forecasting is based on judgments of indi-viduals or groups. The results of qualitative forecasts may be in numerical form but generally are not based on a series of historical data.
Quantitative forecasting, in contrast, generally uses significant amounts of prior data as a basis for prediction. Quantitative techniques can be naive or causal (explana-tory). Naive forecasting projects past data into the future without explaining future trends. Causal or explanatory forecasting attempts to explain the functional relation-ships between the variable to be estimated (the dependent variable) and the variable or variables that account for the changes (the independent variables).
Although judgmental (qualitative) forecasting is used frequently, the use of quantitative methods has been growing rapidly. A survey by the Institute of Business Forecasting reported that time series models that extrapolate past data into the fu-ture were used by 72 percent of the companies surveyed. Simple models (such as av-erages and simple trend) accounted for 60 percent of those using times series, and exponential smoothing (which we will discuss later in this chapter) accounted for 30 percent. “The reason why time series models are used most in businesses is because they are easier to understand and easier to use. Plus, they generally work well for short-term forecasting.” Causal forecasting methods were used by 17 percent and qualitative methods were used in 11 percent of the cases. Surveys were the most popular in the latter category. However, this survey also found that in many cases members of various
19Ibid., p. 309. This quote deals with the merits of relatively simple time series methods versus more com-plex explanatory techniques. “Thus, the evidence suggests that explanatory models do not provide signifi-cantly more accurate forecasts than time series methods, even though the former are much more complex and expensive than the latter” (p. 297).
functions (marketing, production, finance, and sales) of the companies met periodi-cally to review the quantitative forecasts (often called baseline forecasts). “Where nec-essary, they collectively overlay judgment on the baseline forecast.” This was actually done by 83 percent of the companies surveyed. However, a somewhat earlier study of manufacturing companies concluded that judgmental methods are still prevalent in many business organizations.20
Expert Opinion
Various types of techniques fit into the category of expert opinion. Only two are dis-cussed here.
Jury of executive opinion: Forecasts are generated by a group of corporate execu-tives assembled together. The members of the panel may represent different functions within the corporation (intraorganizational) or different corporations (interorganiza-tional). This has been a rather successful technique. However, there is a danger that a panel member who is persuasive but not necessarily knowledgeable may have undue influence on the results. A similar method is to solicit the views of individual salespeo-ple. However, “[o]ften sales people are either overly optimistic or pessimistic. At other times they are unaware of the broad economic patterns that may affect demand.”21 The Delphi Method: This method, developed by the Rand Corporation in the 1950s, is used primarily in predicting technological trends and changes. Delphi also uses a panel of experts. However, they do not meet. The process is carried out by a sequential series of written questions and answers. Although in the past such iterative procedure could have been quite time consuming, computers and e-mail have shortened the pro-cess significantly. The iterations are conducted until the range of answers is narrowed.
Finally, a consensus (or convergence of opinions) is obtained. An early example of this method was a study that asked to forecast five subjects as far as 50 years into the future:
scientific breakthroughs, population growth, automation, space programs, and future weapon systems.22
Although the Delphi method has often been successful, it has drawbacks: “insuf-ficient reliability, oversensitivity of results to ambiguity of questions, different results when different experts are used, difficulty in assessing the degree of expertise, and the impossibility of predicting the unexpected.”23 Given the long-term nature of Delphi predictions, these criticisms do not appear to differ greatly from those leveled against forecasts in general.
Opinion Polls and Market research
You are probably familiar with opinion polling because most of us have at one time or another been subjected to telephone calls or written questionnaires asking us to assess a product or sometimes a political issue. Rather than soliciting experts, opinion polls survey a population whose activity may determine future trends. Opinion polls can be very useful because they may identify changes in trends, which, as we see later
20Chaman L. Jain, “Benchmarking Forecasting Practices in America,” Journal of Business Forecasting, Vol. 25, Issue 4, Winter 2006–07, pp. 9–13; Chaman L. Jain, “Benchmarking Forecasting Models,” Journal of Business Forecasting, Vol. 25, Issue 4, Winter 2006–07, pp. 14–17. Curiously, however, Hanke, Wichern, and Reitsch, Business Forecasting, p. 421, state that “research has shown that, when historical data are available, the judg-ment modification of the forecasts produced by analytical methods tends to reduce the accuracy of the forecasts.” Nada R. Sanders, “The Status of Forecasting in Manufacturing Firms,” Production and Inventory Management Journal, 38, 2, 1997, pp. 32–37.
23Wheelwright and Makridakis, Forecasting Methods, p. 326
22T. J. Gordon and O. Helmer, Report on a Long-Range Forecasting Study, Rand Corporation, P-2982, September 1964. (Described in Harold Sackman, Delphi Critique, Lexington, MA: Lexington Books, 1975, pp. 37–39, 104.)
21Wheelwright and Makridakis, Forecasting Methods, p. 242.
in this chapter, may escape detection when quantitative (both naive and explanatory) methods are used.
Choice of the sample population is of utmost importance because the use of an unrepresentative sample may give completely misleading results. Further, the ques-tions must be stated simply and clearly. Often a question is repeated in a somewhat different form so the replies can be cross-checked.
Market research is closely related to opinion polling. Thorough descriptions of this method can be found in marketing texts. Market research will indicate “not only why the consumer is or is not buying (or is or is not likely to buy), but also who the consumer is, how he or she is using the product, and what characteristics the con-sumer thinks are most important in the purchasing decision.”24 This information can then be used to estimate the market potential and possibly the market share.
Surveys of Spending Plans
The use of surveys of spending plans is quite similar to opinion polling and market research, and the methods of data collection are also quite alike. However, although opinion polling and market research usually deal with specific products and are of-ten conducted by individual firms, the surveys discussed briefly here seek information about “macro-type” data relating to the economy.
1. Consumer intentions. Because consumer expenditure is the largest component of the gross domestic product, changes in consumer attitudes and their effect on subsequent spending are a crucial variable in the forecasts and plans made by businesses. Two well-known surveys are reviewed here.
a. Survey of Consumers, Survey Research Center, University of Michigan. This is probably the best known among the consumer surveys conducted. Initiated in 1946, it is con-ducted monthly. It contains questions regarding personal finances, general business conditions, and buying conditions. The answers to these questions are summarized into an overall index of consumer sentiment and a large number of indexes covering replies to the more detailed questions.
b. Consumer Confidence Survey, The Conference Board. A questionnaire is mailed monthly to a nationwide sample of 5,000 households. Each month a different panel of house-holds is selected. The resulting indexes are based on responses to questions regard-ing business conditions (current and 6 months hence), employment conditions (current and 6 months hence), and expectations regarding family income 6 months hence. The replies are then summarized monthly into three indexes: the Consumer Confidence Index, the Present Situation Index, and the Expectations Index. This survey has been published since 1967.
2. Inventories and sales expectations. A monthly survey published by the National Association of Purchasing Agents is based on a large sample of purchasing executives.
Economic Indicators
The difficult task of predicting changes in the direction of activity is discussed pre-viously. Some of the qualitative techniques discussed in the preceding sections are aimed at identifying such turns. The barometric technique of economic indicators is specifically designed to alert business to changes in economic conditions.
The success of the indicator approach to forecasting depends on the ability to identify one or more historical economic series whose direction not only correlates with, but also precedes that of the series to be predicted. Such indicators are used widely in forecasting general economic activity. Any one indicator series may not be
24Ibid., p. 245.
very reliable; however, a composite of leading indicators can be used to predict. Such a series should exhibit a slowing (and an actual decrease) before overall economic activity turns down, and it should start to rise while the economy is still experiencing low activity.
Forecasting on the basis of indicators has been practiced in an informal fash-ion for many years. It is said that Andrew Carnegie used to assess the future of steel demand by counting the number of chimneys emitting smoke in Pittsburgh. Much of the work of establishing economic indicators was done at the National Bureau of Economic Research, a private organization. Today, economic indicator data are pub-lished monthly by The Conference Board in Business Cycle Indicators. These monthly data are reported in the press and are widely followed.25
There are three major series: leading, coincident, and lagging indicators. As their names imply, the first tells us where we are going, the second where we are, and the third where we have been. Although the leading indicator series is probably the most impor-tant, the other two are also meaningful. The coincident indicators identify peaks and troughs, and the lagging series confirms upturns and downturns in economic activity.
Many individual series are tracked monthly in the Business Cycle Indicators, but only a limited number are used in the construction of the three major indexes. The leading indicator index contains ten series, and the coincident and lagging indicators are made up of four and seven components, respectively. All the series making up the indexes are listed in Table 5.4.
25The data can also be accessed at www.tcb-indicators.org.
Table 5.4 Economic Indicators
Leading indicators
1. Average weekly hours, manufacturing
2. Average weekly claims for unemployment insurance
3. Manufacturers’ new orders, consumer goods and materials industries 4. Index of supplier deliveries—vendor performance
5. Manufacturers’ new orders, nondefense capital goods industries 6. New private housing units authorized by local building permits 7. Stock prices, 500 common stocks—S&P500
8. Real money supply (M2)
9. Interest rate spread, 10-year treasury bonds less federal funds rate 10. Consumer expectations, University of Michigan
Coincident indicators
1. Employees on nonagricultural payrolls 2. Personal income less transfer payments 3. Industrial production index
4. Manufacturing and trade sales Lagging indicators
1. Average duration of unemployment
2. Ratio, manufacturing and trade inventories to sales
3. Change in index of labor cost per unit of manufacturing output 4. Average prime rate charged by banks
5. Commercial and industrial loans outstanding
6. Ratio, consumer installment credit outstanding to personal income 7. Change in consumer price index for services
It is rather evident why some of the indicators qualify as leading. They represent not present expenditures, but commitments indicating that economic activity will take place in the future. Among these are manufacturers’ new orders and building permits.
Others are not quite as obvious. But one would expect employers to increase the hours of their workforce as they increase production before committing themselves to new hiring. Stock market prices and money supply are usually believed to precede cycles.
The month-to-month changes in each component of the series are computed and standardized to avoid undue influence of the more volatile components. The in-dividual series are then combined to create the index.26 The Conference Board also computes a diffusion index that tends to indicate the breadth of the indicator index movement.
How good a forecaster are the leading indicators? To answer this question, we must establish some criteria. First, how many months of change in the direction of the index are necessary before a turn in economic activity is expected? A general rule of thumb is that if, after a period of increases, the leading indicator index sustains three consecutive declines, a recession (or at least a slowing) will follow. On this basis, the lead-ing indicators have predicted each recession since 1948. Second, how much warnlead-ing do the indicators give (i.e., by how many months do they lead) of the onset of a recession?
The following chart shows the lead times in number of months at both peaks and troughs of the business cycle since World War II.27
26An explanation of the methods to compute, update, and standardize the indexes is available on the Web site cited in footnote 25. An excellent source of information on the entire subject of economic indicators is Business Cycle Indicators Handbook, The Conference Board, 2001.
27See any recent issue of Business Cycle Indicators for this information.
Cycle Peak Trough
1948–49 −4 −4
1953–54 −6 −6
1957–58 −23 −2
1960–61 −10 −11
1969–70 −8 −7
1973–75 −9 −2
1980–80 −14 −2
1981–82 −8 −10
1990–91 −18 −2
2001–01 −11 −7
2007–09 −12 −3
It is obvious that the lead times vary considerably and that the lead times at peaks tend to be longer than those at troughs. So we can sum up by observing that leading indicators do warn us about changes in the direction of economic activity, but they really do not forecast lead times reliably. In addition, we must be aware of several other drawbacks:
1. In some instances, the leading indicator index has forecast a recession when none ensued.
2. A decline (or a rebound after a decline) in the index, even if it forecasts correctly, does not indicate the precise size of the decline (or rise) in economic activity.
3. The data are frequently subject to revision in the ensuing months. Thus, the final data may signal a different future from the one suggested by the originally published data.
The preceding criticisms of this forecasting method are certainly significant, but they indicate that this technique should be improved rather than discarded. The
existing indicators are reevaluated periodically, and new ones are developed. As the structure of the economy changes, some of the indicators lose their relevance. For instance, in 1996, shortly after the Conference Board took over the publication of the indexes, two components of the leading indicator index were removed, while one se-ries was added. The present index may still be overly weighted toward manufacturing activities and neglect service industries, which make up a continuously increasing por-tion of our economy. The growing importance of internapor-tional trade and capital flows suggests the inclusion in the index of a series reflecting these activities. In the mean-time, the use of leading indicators has spread to a large number of foreign countries.
In short, despite its drawbacks, the index of leading indicators (and the other two in-dexes as well) is a useful tool for businesspeople and will continue to be closely watched. As always, reliance on this method must be tempered by the knowledge of its imperfections.
Projections
We now turn to trend projections, which we previously identified as a naive form of forecasting. Several different methods are discussed, but they all have a common de-nominator: past data are projected into the future without taking into consideration reasons for the change. It is simply assumed that past trends will continue. Three pro-jection techniques are examined here:
1. Compound growth rate 2. Visual time series projection
3. Time series projection using the least squares method
If annual data are to be forecast, any of these three methods can be used. How-ever, more frequent data, such as monthly or quarterly, may be necessary. If there appear to be significant seasonal patterns in the data, a smoothing method must be applied. The moving-average method of smoothing is discussed, along with the least squares time series projection.
Constant Compound Growth rate
The constant compound growth rate technique is extremely simple and is widely used in business situations. When quick estimates of the future are needed, this method has some merit. And, as we will find out, it can be quite appropriate when the variable to be predicted increases at a constant percentage (as opposed to constant absolute changes).
But care must be exercised not to apply this technique when it is not warranted.
But care must be exercised not to apply this technique when it is not warranted.