Forecasting in business

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 an investor is interested in knowing how long
a recession will last. The investor 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 timeseries
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 timeseries
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:
1. 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.
2. 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.
3. Data concerns. How easy will it be to collect the data
needed to be able to make the forecasts is a significant
issue.
4. Determination of the assumption set. The forecaster
must identify the assumptions that will be made
about the data and the process.

5. Modeling methodology. After careful examination of
the problem, the types of models most appropriate
for the problem must be determined.
6. 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.
7. 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. The steps presented
are the major concerns to the forecaster.

FORECASTING CONCERNS

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 all—period!
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 individual 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