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Summary chapter 3- demand analysis
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Summary Managerial Economics & Business Strategy, ISBN: 0619 Managerial Economics- chapter 3- demand analysis
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Chapter 3: Quantitative Demand AnalysisOwn price elasticity (or point elasticity) – a measure of the responsiveness of the quantity
demanded of a good to a change in the price of that good; the percentage change in quantity
demanded divided by the percentage change in the price of the good.
Perfectly elastic demand – Demand is perfectly elastic if the own price elasticity is
infinite in absolute value. In this case the demand curve is horizontal.
Perfectly inelastic demand – Demand is perfectly inelastic if the own price elasticity is
zero. In this case the demand curve is vertical.
Three factors that affect (the magnitude of) the own price elasticity of a good:
1. Available substitutes. The more substitutes available for the good, the more elastic the
demand for it. Hence, the demand for specific commodities is more elastic than the
demand for broadly defined commodities; e.g. food in general is inelastic, while for
example beef is elastic.
2. Time. The more time consumers have to react to a price change, the more elastic the
demand for the good. Conceptually, time allows the consumer to seek out available
substitutes.
3. Expenditure share. Goods that comprise a relatively small share of consumers’ budgets
tend to be more inelastic than goods for which consumers spend a sizable portion of their
incomes. Hence, the demand for food is more elastic than the demand for transportation.
Cross price elasticity’s play an important role in the pricing decisions of firms that sell multiple
products.
Regression Analysis
Econometrics – statistical analysis of economic phenomena.
Regression Line – line that minimizes the squared deviations between the line (the expected
relation) and the actual data.
Least squares regression: The line that minimizes the sum of squared deviations between the
line and the actual data points. For the equation Y = a + bX + e, this is given by
The parameter estimates, the values of a and b, are often denoted by . They represent values of a
and b that result in the smallest sum of squared errors between a line and the actual data.
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