Demand Estimation & Forecasting

Topics: Regression analysis, Linear regression, Forecasting Pages: 29 (9472 words) Published: April 25, 2011
By studying this unit, you should be able to: identify a wide range of demand estimation and forecasting methods; apply these methods and to understand the meaning of the results; understand the nature of a demand function; identify the strengths and weaknesses of the different methods; understand that demand estimation and forecasting is about minimising risk.

6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 Introduction Estimating Demand Using Regression Analysis Evaluating the Accuracy of the Regression Equation - Regression Statistics The Marketing Approach to Demand Measurement Demand Forecasting Techniques Barometric Forecasting Forecasting Methods: Regression Models Summary Key Words

6.10 Self-Assessment Questions 6.11 Further Readings

The first question which arises is, what is the difference between demand estimation and demand forecasting? The answer is that estimation attempts to quantify the links between the level of demand and the variables which determine it. Forecasting, on the other hand, attempts to predict the overall level of future demand rather than looking at specific linkages. For this reason the set of techniques used may differ, although there will be some overlap between the two. In general, an estimation technique can be used to forecast demand but a forecasting technique cannot be used to estimate demand. A manager who wishes to know how high demand is likely to be in two years’ time might use a forecasting technique. A manager who wishes to know how the firm’s pricing policy could be used to generate a given increase in demand would use an estimation technique. The firm needs to have information about likely future demand in order to pursue optimal pricing strategy. It can only charge a price that the market will bear if it is to sell the product. On one hand, over-optimistic estimates of demand may lead to an excessively high price and lost sales. On the other hand, over-pessimistic estimates of demand may lead to a price which is set too low resulting in lost profits. The more accurate, information the firm has, the less likely it is to take a decision which will have a negative impact on its operations and profitability. The level of demand for a product will influence decisions, which the firm will take regarding the non-price factors that form part of its overall competitive strategy. For example, the level of advertising it carries out will be determined by the perceived need to stimulate demand for the product. As advertising expenditure represents an additional cost to the firm, unnecessary spending in this area needs to


Demandbe avoided. If and Revenue Analysis

the firm’s expectations about demand are too low it may try to compensate by spending large sums on advertising, money which in this instance may be, at least, partly wasted. Alternatively it may decide to redesign the product in response to this, thus incurring unnecessary additional costs in the form of research and development expenditure. In the previous unit, demand analysis was introduced as a tool for managerial decision-making. For example, it was shown that knowledge of price and cross elasticities can assist managers in pricing and that income elasticities provide useful insights into how demand for a product will respond to different macroeconomic conditions. We assumed that these elasticities were known or that the data were already available to allow them to be easily computed. Unfortunately, this is not usually the case. For many business applications, the manager who desires information about elasticities must develop a data set and use statistical methods to estimate a demand equation from which the elasticities can then be calculated. This estimated equation could then, also be used to predict demand for the product, based on assumptions about prices, income, and other factors. In this unit the basic techniques of demand...
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