Business leaders and economists are continually involved in the process of trying to forecast, or predict, the future of business in the economy. Business leaders engage in this process because much of what happens in businesses today depends
on what is going to happen in the future. For example, if a business is trying to make a decision about developing a revolutionary new automobile, it would be nice to know whether the economy is going to be in a recession or whether it will be booming when the automobile is released to the general public. If there is a recession, consumers will not buy the automobile unless it can save them money, and the manufacturer will have spent millions or billions of dollars on the development of a product that might not sell.
The process of attempting to forecast the future is not new. Most ancient civilizations used some method for predicting the future. Today, computers with elaborate programs are often used to develop models to forecast future economic and business activity. Contemporary models of economic and business forecasting have been developed in the last century. Today's forecasting models are considerably more statistical than they were hundreds of years ago when the stars, and other mystical methods, were used to predict the future. Almost every large business or government agency performs some type of formalized forecasting.
Forecasting in business is closely related to understanding the business cycle. The foundations of modern forecasting were laid in 1865 by William Stanley Jevons, who argued that manufacturing had replaced agriculture as the dominant sector in English society. He studied the effects of economic fluctuations of the limiting factors of coal production on economic development.
Forecasting has become big business around the world. Forecasters try to predict what the stock markets will do, what the economy will do, what numbers to pick in the lottery, who will win sporting events, and almost anything one might name. Regardless of who does it, forecasting is done to identify what is likely to happen in the future so as to be able to benefit most from the events.
QUALITATIVE FORECASTING MODELS
Qualitative forecasting models have often proven to be most effective for short-term projections. In this method of forecasting, which works best when the scope is limited, experts in the appropriate fields are asked to agree on a common forecast. Two methods are used frequently.
Delphi Method. This method involves asking various experts what they anticipate will happen in the future relative to the subject under consideration. Experts in the automotive industry, for example, might be asked to forecast likely innovative enhancements for cars five years from now. They are not expected to be precise, but rather to provide general opinions.
Market Research Method. This method involves surveys and questionnaires about people's subjective reactions to changes. For example, a company might develop a new way to launder clothes; after people have had an opportunity to try the new method, they would be asked for feedback about how to improve the processes or how it might be made more appealing for the general public. This method is difficult because it is hard to identify an appropriate sample that is representative of the larger audience for whom the product is intended.
QUANTITATIVE FORECASTING MODELS
Three quantitative methods are in common use.
Time-Series Methods. This forecasting model uses historical data to try to predict future events. For example, assume that you are interested in knowing how long a recession will last. You might look at all past recessions and the events leading up to and surrounding them and then, from that data, try to predict how long the current recession will last.
A specific variable in the time series is identified by the series name and date. If gross domestic product (GDP) is the variable, it might be identified as GDP2000.1 for the first-quarter statistics for the year 2000. This is just one example, and different groups might use different methods to identify variables in a time period.
Many government agencies prepare and release time-series data. The Federal Reserve, for example, collects data on monetary policy and financial institutions and publishes that data in the Federal Reserve Bulletin. These data become the foundation for making decisions about regulating the growth of the economy.
Time-series models provide accurate forecasts when the changes that occur in the variable's environment are slow and consistent. When large-degree changes occur, the forecasts are not reliable for the long term. Since time-series forecasts are relatively easy and inexpensive to construct, they are used quite extensively.
The Indicator Approach. The U.S. government is a primary user of the indicator approach of forecasting. The government uses such indicators as the Composite Index of Leading, Lagging, and Coincident Indicators, often referred to as Composite Indexes. The indexes predict by assuming that past trends and relationships will continue into the future. The government indexes are made by averaging the behavior of the different indicator series that make up each composite series.
The timing and strength of each indicator series relationship with general business activity, reflected in the business cycle, change over time. This relationship makes forecasting changes in the business cycle difficult.
Econometric Models. Econometric models are causal models that statistically identify the relationships between variables and how changes in one or more variables cause changes in another variable. Econometric models then use the identified relationship to predict the future. Econometric models are also called regression models.
There are two types of data used in regression analysis. Economic forecasting models predominantly use time-series data, where the values of the variables change over time. Additionally, cross-section data, which capture the relationship between variables at a single point in time, are used. A lending institution, for example, might want to determine what influences the sale of homes. It might gather data on home prices, interest rates, and statistics on the homes being sold, such as size and location. This is the cross-section data that might be used with time-series data to try to determine such things as what size home will sell best in which location.
An econometric model is a way of determining the strength and statistical significance of a hypothesized relationship. These models are used extensively in economics to prove, disprove, or validate the existence of a casual relationship between two or more variables. It is obvious that this model is highly mathematical, using different statistical equations.
For the sake of simplicity, mathematical analysis is not addressed here. Just as there are these qualitative and quantitative forecasting models, there are others equally as sophisticated; however, the discussion here should provide a general sense of the nature of forecasting models.
THE FORECASTING PROCESS
When beginning the forecasting process, there are typical steps that must be followed. These steps follow an acceptable decision-making process that includes the following elements:
- Identification of the problem. Forecasters must identify what is going to be forecasted, or what is of primary concern. There must be a timeline attached to the forecasting period. This will help the forecasters to determine the methods to be used later.
- Theoretical considerations. It is necessary to determine what forecasting has been done in the past using the same variables and how relevant these data are to the problem that is currently under consideration. It must also be determined what economic theory has to say about the variables that might influence the forecast.
- Data concerns. How easy will it be to collect the data needed to be able to make the forecasts is a significant issue.
- Determination of the assumption set. The forecaster must identify the assumptions that will be made about the data and the process.
- Modeling methodology. After careful examination of the problem, the types of models most appropriate for the problem must be determined.
- Preparation of the forecast. This is the analysis part of the process. After the model to be used is determined, the analysis can begin and the forecast can be prepared.
- Forecast verification. Once the forecasts have been made, the analyst must determine whether they are reasonable and how they can be compared against the actual behavior of the data.
Each of the seven steps has substages; however, the steps that have been presented are the major concerns to the forecaster. Those with a deep interest in forecasting might pursue more in-depth treatments.
Forecasting does present some problems. Even though very detailed and sophisticated mathematical models might be used, they do not always predict correctly. There are some who would argue that the future cannot be predicted at allperiod!
Some of the concerns about forecasting the future are that (1) predictions are made using historical data, (2) they fail to account for unique events, and (3) they ignore coevolution (developments created by our own actions). Additionally, there are psychological challenges implicit in forecasting. An example of a psychological challenge is when plans based on forecasts that use historical data become so confining as to prohibit management freedom. It is also a concern that many decision makers feel that because they have the forecasting data in hand they have control over the future.
Regardless of the opponents to forecasting, the U.S. government, investment analysts, business managers, economists, and numerous others will continue to use forecasting techniques to predict the future. It is imperative for the users of the forecasts to understand the information and use the results as they are intended.
Fulmer, William, E. (2000). Shaping the Adaptive Organization: Landscapes, Learning, and Leading in Volatile Times. New York: AMACOM.
Moore, Geoffrey H. (1983). Business Cycles, Inflation, and Forecasting. Cambridge, MA: Ballinger.
Sherman, Howard, J., and Kolk, David X. (1996). Business Cycles and Forecasting. New York: HarperCollins.
Stock, James H., and Watson, Mark W., eds. (1993). Business Cycles, Indicators, and Forecasting. Chicago: University of Chicago Press.
Did this raise a question for you?