COMMON TYPES OF FORECASTING PROBLEMS - Managerial Economics

Macroeconomic Forecast Problems

Macroeconomic forecasting involves predicting aggregate measures of economic activity at the international, national, regional, or state level. Predictions of gross domestic product (GDP), unemployment, and interest rates by “blue chip” business economists capture the attention of national media, business, government, and the general public on a daily basis.2 Other macroeconomic forecasts commonly reported in the press include predictions of consumer spending, business investment, homebuilding, exports, imports, federal purchases, state and local government spending, and so on. Macroeconomic predictions are important because they are used by businesses and individuals to make day-to-day operating decisions and long-term planning decisions. If interest rates are projected to rise, homeowners may rush to refinance fixed-rate mortgages, while businesses float new bond and stock offerings to refinance existing debt or take advantage of investment opportunities.

When such predictions are accurate, significant cost savings or revenue gains become possible. When such predictions are inaccurate, higher costs and lost marketing opportunities occur.

The accuracy of any forecast is subject to the influence of controllable and uncontrollable factors. In the case of macroeconomic forecasting, uncontrollable factors loom large. Take interest rate forecasting, for example. The demand for credit and short-term interest rates rises if businesses seek to build inventories or expand plant and equipment, or if consumers wish to increase installment credit. The supply of credit rises and short-term interest rates fall if the Federal Reserve System acts to increase the money supply, or if consumers cut back on spending to increase savings. Interest rate forecasting is made difficult by the fact that business decisions to build inventories, for example, are largely based on the expected pace of overall economic activity—which itself depends on interest-rate expectations. The macroeconomic environment is interrelated in ways that are unstable and cannot be easily predicted.

Even policy decisions are hard to predict. For example, Federal Reserve System policy meeting minutes are confidential until months after the fact. Is it any wonder that “Fed watching” is a favorite pastime of business economists?

Microeconomic Forecast Problems

In contrast with macroeconomic forecasting, microeconomic forecasting involves the prediction of disaggregate economic data at the industry, firm, plant, or product level. Unlike predictions of GDP growth, which are widely followed in the press, the general public often ignores microeconomic forecasts of scrap prices for aluminum, the demand for new cars, or production costs for Crest toothpaste. It is unlikely that the CBS Evening News will ever be interrupted to discuss an upward trend in used car prices, even though these data are an excellent predictor of new car demand. When used car prices surge, new car demand often grows rapidly; when used car prices sag, new car demand typically drops. The fact that used car prices and new car demand are closely related is not surprising given the strong substitute good relation that exists between used cars and new cars.

Trained and experienced analysts often find it easier to accurately forecast microeconomic trends, such as the demand for new cars, than macroeconomic trends, such as GDP growth. This is because microeconomic forecasts abstract from the multitude of interrelationships that together determine the macroeconomy. With specialized knowledge about changes in new car prices, car import tariffs, car loan rates, and used cars prices, among other factors, it is possible to focus on the fairly narrow range of important factors that influence new car demand. In contrast, a similarly precise model of aggregate demand in the macroeconomy might involve thousands of economic variables and hundreds of functional relationships. This is not to say that precise microeconomic forecasting is easy. For example, in August 1999, Standard and Poor’s DRI forecast new car and light truck sales of 15.7 million units for the 2000 model year. This was a reasonable number, and within the 15.3–16.0 million unit range of forecasts provided by the University of Michigan, Blue Chip Economic Forecasters, and others. Unfortunately, in September 2000, all such forecasts proved too conservative in light of the 17.2 million units actually sold in a robust economic environment. Undaunted, forecasters expected unit sales of 16.1 million in 2001 and 16.8 million in 2002. Those numbers looked good, until terrorist attacks in New York City and Washington, DC, on September 11, 2001, sent new car and light truck sales into a tailspin as consumer confidence plummeted.

At that point, it became anybody’s guess as to how long it would take for consumer confimicroeconomic dence and new car and light truck sales to recover. Obviously, accurate auto and light truck demand forecasting is tough even for industry experts.

Problem of Changing Expectations

The subtle problem of changing expectations bedevils both macroeconomic and microeconomic forecasting. If business purchasing agents are optimistic about future trends in the economy and boost inventories in anticipation of surging customer demand, the resulting inventory buildup can itself contribute to economic growth. Conversely, if purchasing agents fear an economic downturn and cut back on orders and inventory growth, they themselves can be a main contributor to any resulting economic downturn. The expectations of purchasing agents and other managers can become a self-fulfilling prophecy because the macroeconomic environment represents the sum of the investment and spending decisions of business, government, and the public. In fact, the link between expectations and realizations has the potential to create an optimistic bias in government-reported statistics. Government economists are sometimes criticized for being overly optimistic about the rate of growth in the overall economy, the future path of interest rates, or the magnitude of the federal deficit. As consumers of economic statistics, managers must realize that it can pay for government or politically motivated economists to be optimistic. If business leaders can be led to make appropriate decisions for a growing economy, their decisions can in fact help lead to a growing economy. Unlike many business economists from the private sector, government employed and / or politically motivated economists often actively seek to manage the economic expectations of business leaders and the general public.

It is vital for managers to appreciate the link between economic expectations and realizations, and to be wary of the potential for forecast bias.

Data Quality Problems

Accurate forecasts require pertinent data that are current, complete, and free from error. Almost everyone has heard the familiar warning about the relation between data quality and forecast accuracy: “garbage in, garbage out.” However, this statement is true in ways that are not immediately obvious. For example, if a manager wants to forecast demand for consumer or producer goods, it is often better to input incoming orders rather than shipments because shipments are sometimes subject to production delays. Similarly, the timing of order fulfillment is sometimes subject to delays in transit that are beyond the control of the shipping firm. In addition to carefully considering the quality of data used to generate forecasts, the quantity of available data is also important. A general rule is: The more data that can be subject to analysis, the better. Some advanced forecasting software that works on desktop personal computers can function with as few as five data points. However, forecasts that result from such paltry bodies of data are often simplistic, if not trivial. Although the collection of large samples of data on market transactions can be expensive and tedious, the payoff in forecast accuracy can justify the effort.

If monthly data are seasonal in nature, it is important to have an extended time series to facilitate forecast accuracy. Most forecasting software programs used to monitor monthly activity require a minimum of 2 years of data (24 observations) to build a seasonally adjusted forecast model. Practically speaking, 2 years of monthly data are often not enough; 5 years of monthly data (60 observations) are typically necessary before a high level of monthly forecast accuracy can be achieved. Of course, most forecast software works with data of any periodicity, be it hourly, daily, weekly, monthly, or annual in nature. The ultimate consideration that must be addressed is whether the quantity and quality of data analyzed are sufficient to shed meaningful light on the forecast problem being addressed. The acid test is: Can useful forecasts be generated?

One of the most vexing data quality problems encountered in forecasting is the obstacle presented by government-supplied data that are often tardy and inaccurate. For example, the Commerce Department’s Bureau of Economic Analysis “advanced” estimate of GDP for the fourth quarter of the year is typically published in late January of the following year. A “preliminary” revision to this estimate is then released by the Bureau of Economic Analysis on March 1; an official final revision is not made available until March 31, or until 90 days after the fact. Such delays induce uncertainty for those seeking to make projections about future trends in economic activity. Worse still, preliminary and final revisions to official GDP estimates are often large and unpredictable. Extreme variation in official estimates of key economic statistics is a primary cause of forecast error among business economists. Finally, it is worth remembering that forecasts are, by definition, never perfect. All forecasting methods rely heavily on historical data and historical relationships. Future events are seldom, if ever, explicitly accounted for in popular forecasting techniques. Managers must combine traditional forecast methods with personal insight and knowledge of future events to create the most useful forecasts.

Common Forecast Techniques

Some forecasting techniques are basically quantitative; others are largely qualitative. The most commonly applied forecasting techniques can be divided into the following broad categories:

  • Qualitative analyses
  • Trend analysis and projection
  • Exponential smoothing
  • Econometric methods

The best forecast methodology for a particular task depends on the nature of the forecasting problem. When making a choice among forecast methodologies, a number of important factors must be considered. It is always worth considering the distance into the future that one must forecast, the lead time available for making decisions, the level of accuracy required, the quality of data available for analysis, the stochastic or deterministic nature of forecast relations, and the cost and benefits associated with the forecasting problem.

Trend analysis, market experiments, consumer surveys, and the leading indicator approach to forecasting are well suited for short-term projections. Forecasting with complex econometric models and systems of simultaneous equations have proven somewhat more useful for longrun forecasting. Typically, the greater the level of sophistication, the higher the cost. If the required level of accuracy is low, less sophisticated methods can provide adequate results at minimal cost.


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