E303lect23F09

E303lect23F09 - October 19, 2009 Reading: Ch. 3, pp. 95-107...

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October 19, 2009 Reading: Ch. 3, pp. 95-107 Collect HW Problem Set #6 HW Problem Set #7, posted. Due Wed? I will be available in the lab for help with HW#7 Lecture 23 REVIEW___________________________________________________ III. Quantitative Demand Analysis C.Elasticities and Demand Functions PREVIEW___________________________________________________ D. Estimating Demand: Regression Analysis. Introduction 1. The Bivariate Case 2. Doing Regressions with EXCEL a. The Bivariate Case b. A multivariate case c. Interpreting regression output R 2 MSE 3. Interpreting Significance of Parameter Estimates LECTURE_________________________________________________ D . Estimating Demand: Regression Analysis. 1. Overview. Consider the relationship Y = A + B 1 X + B 2 P + B 3 I + B 4 P r Where Y = qty. demanded X = advertising and promotional expenses P = price of a good P r = price of a related (competing) good. We would like to know how to get values for the parameters A, B 1 , B 2 , B 3 , and B 4 . There are a number of ways to collect the data that would allow estimation of a demand function. Possibilities include market experiments , surveys and regression analysis . Surveys are limited because people don’t respond well to hypothetical questions. Market experiments can provide great data, but they are very expensive. Regression analysis uses data that arises naturally in the sales process to estimate data. Regression analysis is inexpensive and fairly easy to do. Our intention in this section is to briefly overview the process of constructing estimates with regressions. (The lessons we learn here apply broadly to many of the topics we’ll study. However, here we focus on the estimation of the parameters in a demand function.) 1
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2. The Bivariate Case . Suppose in some simple world, sales are only affected by advertising expenditures. Assume also that the factors are linearly related. Then we have Y = A + B 1 X. Suppose further, however, that this specification is a model - by assumption a simplification from the natural world. Suppose that there is some random error e in our estimate. That is, for each observation i , Y i = A + B 1 X i + e i e i has a mean of 0. Graphically Q B } A A This is called a population regression line (or the true underlying relationship). Of course, we don't see the underlying population regression line. Rather, we must try to estimate it from available data. The general expression for this sample estimate is
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This note was uploaded on 04/12/2010 for the course ECON 303 taught by Professor Shrestha during the Fall '08 term at VCU.

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E303lect23F09 - October 19, 2009 Reading: Ch. 3, pp. 95-107...

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