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From |
"Tam Phan" <tamdphan@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Regression Techniques |

Date |
Thu, 21 Feb 2008 22:04:14 -0400 |

Hello Stata Community: I have recently encountered two methodology of linear regression techniques. The main objective of the two techniques is to establish the effects of price on the demand of certain products/items. Below are two techniques outlined: (1) Y=a+X'b+e where X= explanatory variables, excluding price, Y is the observed quantity purchased for a particular product (2) Ynorm=e+average(Y) (3) Ynorm= a + b(price)+Ei After performing regression in (1), Ynorm is calculated by the sum of the residuals and the average of the original Y. This Ynorm is then regress with price as the single explanatory variable. The claim is that the fitted values in (3) will produce the "demand" of a product with only the effects of price and Ei. What are your thoughts on this? Technique two: (1) Y= a + X'b1 + b2(price) +e (2) Ynorm = a +b1*(average(X)) + b(2price) +e Technique two only has one stage of regression (1), then the demand is "normalize" by multiplying the coefficients by the average of their respected explanatory variables, then whats left over is the quantity sold, in terms of price. Again, what are your thoughts? Which technique is "better?" Advantages/disadvantages? TP * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Regression Techniques***From:*"Austin Nichols" <austinnichols@gmail.com>

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