需求分析与需求估计.ppt

LOGO 第四讲 需求分析与需求估计 主要内容 一 需求关系 二 需求的价格弹性 三 需求的收入弹性 四 需求的交叉弹性 五 弹性在管理决策中的应用 六 需求估计的市场研究方法 七 需求函数的统计估计 经济分析与决策 n 需求分析 n 生产和成本分析 n 产品、定价和产出量决策 n 资本支出分析 经济、政治与社会环境 n经营状况（趋势、周期和季节性影响） n要素市场状况（资本、劳动和原材料） n竞争者反应与策略性反应 n组织架构与规则限制 现金流量风险 厂商价值 （股东的财富） Health Care & Cigarettes v Raising cigarette taxes reduces smoking In Canada, $4 for a pack of cigarettes reduced smoking 38% in a decade v But cigarette taxes also helps fund health care initiatives The issue then, should we find a tax rate that maximizes tax revenues? Or a tax rate that reduces smoking? 一、需求关系 n 需求表、需求曲线与需求函数。 n 消费者的基本问题花得起的条件下最大化效用 需求函数。 n 价格变化引起需求量的变化。 收入效应； 替代效应。 n 耐用品与非耐用品 n 对生产者的派生需求。 v Reasons that price and quantity are negatively related include: income effect - as the price of a good declines, the consumer can purchase more of all goods since his or her real income increased. substitution effect - as the price declines, the good becomes relatively cheaper. A rational consumer maximizes satisfaction by reorganizing consumption until the marginal utility in each good per dollar is equal: 二、需求的价格弹性 n 需求的决定因素厂商是否可控？这些因素变化对需求 量的影响有多大？定量！ n 案例3-1两种不同机制：Ford & Pizzahut。 n 价格弹性 Price Elasticity ED = % change in Q / % change in P Shortcut notation: ED = %Q / %P A percentage change from 100 to 150 is 50% A percentage change from 150 to 100 is -33% For arc elasticities, we use the average as the base, as in 100 to 150 is +50/125 = 40%, and 150 to 100 is -40% Arc Price Elasticity - averages over the two points D arc price elasticity ED = DQ/ (Q1 + Q2)/2 DP/ (P1 + P2)/2 Average price Average quantity 二、需求的价格弹性 n 计算实例1P63李维斯牛仔裤的需求弹性。 n 计算实例2P63NBA公司索价。 n 案例3-2麦肯电讯。 n 价格与收益的关系 表3-4。图3-2。 当需求弹性绝对值1时，价格上升（下降）将造成消 费者的总支出的的减少（增加）。 当需求弹性绝对值=1时，价格上升（下降）将造成消 费者的总支出的的不变。 If ED = -1, unit elastic If ED -1, inelastic, e.g., - 0.43 If ED 1）。 n 奢侈品一般具有高收入弹性，必需品具有低收入弹 性 If EY 0, then it is a normal or income superior good some goods are Luxuries: EY 1 with a high income elasticity some goods are Necessities: EY 0；互补品EX Critical-t, then REJECT Ho. We say that its significant! v The estimated t = (b - 0) / b v The critical t is: Large Samples, critical t2 N 30 Small Samples, critical t is on Students t Distribution, page B-3 at end of book, usually column 0.05. D.F. = # observations, minus number of independent variables, minus one. N 2.306, so we reject the null hypothesis. We informally say, that promotional expenses (X) is “significant.” Correlation Coefficient v We would expect more promotional expenditures to be associated with more sales at Sherwin-Williams. v A measure of that association is the correlation coefficient, r. v If r = 0, there is no correlation. If r = 1, the correlation is perfect and positive. The other extreme is r = -1, which is negative. r = -1 r = +1 r = 0 Y Y Y X X X Coefficient of Determination: R2 v R-square is the percentage of the variation in dependent variable that is explained v R2 = Yt -Yt 2 / Yt - Yt 2 = SSR / SST v As more variables are included, R-square rises v Adjusted R-square, however, can decline _ X Y _ Y Yt Yt predicted Association and Causation v Regressions indicate association, but beware of jumping to the conclusion of causation v Suppose you collect data on the number of swimmers at a beach and the temperature and find: v Temperature = 61 + .04 Swimmers, and R2 = .88. Surely the temperature and the number of swimmers is positively related, but we do not believe that more swimmers CAUSED the temperature to rise. Furthermore, there may be other factors that determine the relationship, for example the presence of rain or whether or not it is a weekend or weekday. v Education may lead to more income, and also more income may lead to more education. The direction of causation is often unclear. But the association is very strong. Multiple Linear Regression v Most economic relationships involve several variables. We can include more independent variables into the regression. v To do this, we must have more observations (N) than the number of independent variables, and no exact linear relationships among the independent variables. v At Sherwin-Williams, besides promotional expenses, different regions charge different selling prices (SellPrice) and have different levels of disposable income (DispInc) v The next slide gives the output of a multiple linear regression, multiple, because there are three independent variables Figure 4.9 Dep var: Sales (Y) N=10 R-squared = .790 Adjusted R2 = .684 Standard Error of Estimate = 17.417 Variable Coefficient Std error T P Constant310.245 95.075 3.263 .017 Promotion .008 0.204 0.038 .971 SellPrice -12.202 4.582 -2.663 .037 DispInc 2.677 3.160 0.847 .429 Interpreting Multiple Regression Output v Write the result as an equation: Sales = 310.245 + .008 Promotion -12.202 SellPrice + 2.677 DispInc vDoes the result make economic sense? As promotion expense rises, so does sales. That makes sense. As the selling price rises, so does sales. Yes, thats reasonable. As disposable income rises in a region, so does sales. Yup. Thats reasonable. v Is the coefficient on the selling price statistically significant? The estimated t value is given in Figure 4.9 to be -2.663 The critical t value, with 6 ( which is 10 3 1) degr