Causal Forecasting

Some forecasting methods try to identify the underlying factors that might influence the variable that is being forecast. For example, including information about climate patterns might improve the ability of a model to predict umbrella sales. Forecasting models often take account of regular seasonal variations. In addition to climate, such variations can also be due to holidays and customs: for example, one might predict that sales of college football apparel will be higher during the football season than during the off season.

Several informal methods used in causal forecasting do not employ strict algorithms, but instead use the judgment of the forecaster. Some forecasts take account of past relationships between variables: if one variable has, for example, been approximately linearly related to another for a long period of time, it may be appropriate to extrapolate such a relationship into the future, without necessarily understanding the reasons for the relationship.

Chain-ratio method

An estimation method used by multiplying a base number by a chain of related percentages. It is frequently used in marketing to estimate the size of a target market. It is a method of calculating total market demand for a product in which a base number, such as the total population of a country, is multiplied by several percentages, such as the number in the population above and below certain ages, the number in the population with an interest in motor sport, the number in the population with motor-cycle licences, in order to arrive at a rough estimate of the potential demand for a particular good or service (in this case, say, a new type of motor cycle helmet.

Here is a step-by-step example of using the Chain Ratio Method to estimate the number of TVs in the United States (numbers for illustration only):

Question: How many TVs are in the US?

Method:

• There are the roughly 300 million people in the US (This is the base number).
• There are roughly 4 people per household: 300,000,000/4 = 75,000,000 households.
• There are roughly 2 TVs per household: 75,000,000 * 2 = 150,000,000 TVs.

Answer:

150 million TVs

Consumption Level Method

This method is used for those products that are directly consumed. This method measures the consumption level on the basis of elasticity coefficients.  The important ones are

Income Elasticity – This reflects the responsiveness of demand to variations in income. It is calculated as –

E1   = [Q2 – Q1/ I2- I1] * [I1+I2/ Q2 +Q1]

Where,

E1 = Income elasticity of demand,      Q1 = quantity demanded in the base year,

Q2 = quantity demanded in the following year,           I1 = income level in the base year, and

I2 = income level in the following year

Price Elasticity – This reflects the responsiveness of demand to variations in price. It is calculated as

EP   = [Q2 – Q1/ P2- P1] * [P1+P2/ Q2 +Q1]

Where,

EP   = Price elasticity of demand         Q1 = quantity demanded in the base year

Q2 = quantity demanded in the following year            P1     = price level in the base year

P2   = price level in the following year

• End use method – This method forecasts the demand based on the consumption coefficient of the various uses of the product.
• Leading indicator method – This method uses the changes in the leading indicators to predict the changes in the lagging indicators.
• Econometric method – An advanced forecasting tool, it is a mathematical expression of economic relationships derived from economic theory.