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Business Forecasting and Modeling
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The Need to Forecast

When the result of an action is of consequence, but cannot be known in advance with precision, forecasting may reduce decision risk by supplying additional information about the possible outcome. The potential benefit of forecasting lies in the realm of decision making to exert control over some process. To be able to look into future is always a question in mind of decision makers. Calculating the likely value of a variable or its likely behavior are important outcomes of forecasting.

Decision theorists mention two types: passive and active. Active control may be exerted if there is some possibility of altering the direction or magnitude of some process under the firm's management. Even if such active control is not possible under known technologies (e.g., the weather or the implementation of a government policy or a competitor's R&D efforts), there may be some possibility of exerting passive control. Passive control is anticipatory of the likely consequence--for example to move out of the way or to erect barriers or other means of protection from the phenomenon, or even to move to take advantage of such likely changes.

There are basically three questions to answer: When might an event occur? What are the qualitative characteristics of the outcome of an expected event? What will be the magnitude of a quantity at a future point in time?

Event-timing forecasts can perhaps be best approached by seeking to identify some sort of leading indicators (e.g., the date at which a terminal illness is diagnosed) like in Security analysis and portfolio management. Qualitative-outcome forecasting is best approached by seeking to add to the stock of information upon which probability assessments may be based (e.g., almost 90 percent of all sales personnel in our industry are females). Quantitative-magnitude forecasts may be approached by examining series of historical data about the target phenomenon and related matters. The default forecast, i.e., future will be like the recent past, may be entirely adequate simpler and less-consequential decision-making circumstances of commercial operations. Many aspects of the world are more complex and dynamic, however.

Computational vs. Judgmental Forecasting

A purely judgmental approach to forecasting might avoid the use of computational techniques altogether. The exclusively judgmental approach relies upon the perceptiveness, insight, and experience of the forecaster to produce the forecast of the future state. Depending upon how consequential the decision and how able the forecaster, a purely judgmental approach may yield satisfactory forecasts. In cases of more consequential decisions or more dynamic situations, computational forecasting methods may be in order. However, it does not necessarily follow that a computational approach will be able to improve upon informed judgment.


The forecasting techniques consist of one important type known as Model methodology. A brief review of model formats will provide a platform for extending them in the realm of forecasting. If continuous variation may be depicted in two- or three-dimensional graphic space, the model may be represented in mathematical form as a generalized functional-notation statement, e.g.

(1) Y = f(x), or as a specific equation, such as (2) Y = a + bx,

First, a lead-lag structure needs to be built into the independent-dependent variable relationship. Otherwise, we shall find ourselves in the difficult position of having to predict values of the independent variables before we can forecast values of the dependent variable. The structuring of a lead-lag relationship may be accomplished by pairing dependent variable values with independent variable observations occurring one or more periods earlier in time. Equation (2) may thus be recast as

(3) Yt = a + bxt-1

Whatever be the source of information, the pile of numerical data may be sliced in either of two possible directions. First, observations of various aspects of a phenomenon or process may be recorded at a point in time, but across the population of subjects. Data collected in this fashion are generally referred to as cross-sectional data. Alternately, observations of the behavior of a single entity may be recorded at various points in time, either at regular intervals or irregularly. If the time interval between subsequent observations is a constant, for example, a month or a year, the collection of data may be referred to as a time series.

Specification of a Forecasting Model

The process of specifying a forecasting model involves (1) selecting the variables to be included, (2) selecting the form of the equation of relationship, and (3) estimating the values of the parameters in that equation. After the model is specified, (4) its performance characteristics should be verified or validated by comparison of its forecasts with historical data for the phenomenon it was designed to forecast.

The Data Matrix

Data may be collected from experiments, by conducting surveys, or from historical sources. Regardless of the source of the data, a convenient form in which to organize it for statistical analysis is the data matrix which is a rectangular array of numbers presented in a row-and-column format. The columns and rows may be given either possible identity as desired, but for our purposes it will be convenient to construe the columns as "variables," and the rows as "cases" or "observations."


After the matrix has been populated with original data, the analyst may find that other versions or transformations of the data in certain columns are needed in some of the forecasting models. For example, it may be desirable to lag or lead all of the values in one of the columns relative to data in the other columns. This would require that the data in that column be shifted upward or downward by the requisite number of rows. In a regression model, it may be desirable to use the squared or logarithmic values of the data in some column as a dependent or independent variable. If such data transformations are required, it will be convenient in structuring the data matrix to allow several vacant columns beyond the original data columns to receive the transformed data.

Once a potentially usable data series has been entered into a matrix, the analyst must consider criteria for selecting appropriate forecasting models. Various statistical and graphic techniques may help in selecting a forecasting model. A time series exhibits trend if the data path is not approximately level, but changes consistently in the same direction; trend is present if the means for successive ranges of the time series consistently increase (or decrease). Economists disagree as to whether there are natural irrevocable wave-like processes in commercial data which can be called Cycles, or only discrete fluctuations resulting from the impacts of exogenous occurrences or policy actions. The term "cycle" is taken to refer to alternations in runs of values above and below the mean of a time series. Seasonality is cyclical behavior with a period of one year and which repeats itself year after year; seasonality cannot be seen at all in annual data and can be seen only imperfectly in quarterly data.

Most monthly time series of economic or business data typically contain two or more of these components which may be discerned by visual inspection of a sequence plot of the data. A scatter diagram plotting dependent variable data against data for an independent variable may indicate the predictive ability of the independent variable. A statistical correlation matrix can enable the analyst to assess the strength of relationships between the object series and other possible predictor series.

What is a Model?

A Model is an external and explicit representation of a part of reality, as it is seen by individuals who wish to use this model to understand, change, manage and control that part of reality. The field of Operations research is attempt of applying models to business problems. Simulation is one such powerful model to predict requirement of resources.

Descriptive and prescriptive models: A descriptive model is often a function of figuration, abstraction based on reality. However, a prescriptive model is moving from reality to a model a function of development plan, means of action, moving from model to the reality.

One must distinguishes between descriptive and prescriptive models in the perspective of a traditional analytical distinction between knowledge and action. The prescriptive models are in fact the furthest points in a chain cognitive, predictive, and decision making.

Validation and Verification: As part of the calibration process of a model, the modeler must validate and verified the model. The term validation is applied to those processes, which seek to determine whether or not a model is correct with respect to the "real" system. More prosaically, validation is concerned with the question "Are we building the right system?" Verification, on the other hand, seeks to answer the question "Are we building the system right?"

Some examples for Financial Economics decisions are: Markov Chains; Leontief's Input-Output Model; Risk as a Measuring Tool and Decision Criterion; Break-even and Cost Analyses; Modeling the Bidding Process; Product's Life Cycle Analysis and Forecasting.

Some special Modeling techniques examples are: Neural Network; Modeling and Simulation; Probabilistic Models; Event History Analysis; Predicting Market Response; Prediction Interval for a Random Variable; Census II Method of Seasonal Analysis; Delphi Analysis and System Dynamics Modeling


There are huge number of models in forecasting for financial, marketing, economic, and scientific use and so many other purposes. But very few of them are used regularly. The collection of data can be through secondary sources, company records of past, experiments on work floor or in laboratories, observations from proximity or from distance, interviews and questionnaires. The study can be one time i.e., cross sectional or longitudinal or time series. Both qualitative and quantitative models have been developed. Modeling is very important tool of forecasting and so is most commonly used time series analysis.

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