Lecture+18Multiple+Regression+Analysis+-+Dummies

# Lecture+18Multiple+Regression+Analysis+-+Dummies - Lecture...

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Lecture 18, ECON 123A, Fall 2011 18-1 Dale J. Poirier Lecture 18 Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables C In previous chapters, the dependent and independent variables in our multiple regression models have had quantitative meaning. B Just a few examples include hourly wage rate, years of education, college grade point average, amount of air pollution, level of firm sales, and number of arrests.

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Lecture 18, ECON 123A, Fall 2011 18-2 Dale J. Poirier B In each case, the magnitude of the variable conveys useful information. C In empirical work, we must also incorporate qualitative factors into regression models. B The gender or race of an individual, the industry of a firm (manufacturing, retail, etc.), and the region in the United States where a city is located (south, north, west, east) are all considered to be qualitative factors. B Most of this chapter is dedicated to qualitative independent variables.
Lecture 18, ECON 123A, Fall 2011 18-3 Dale J. Poirier 7.1 Describing Qualitative Information C Qualitative factors often come in the form of binary information: a person is female or male; a person does or does not own a personal computer; a firm offers a certain kind of employee pension plan or it does not; a state administers capital punishment or it does not. C In econometrics, binary variables are most commonly called dummy variables , although this name is not especially descriptive.

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Lecture 18, ECON 123A, Fall 2011 18-4 Dale J. Poirier C In defining a dummy variable, we must decide which event is assigned the value one and which is assigned the value zero. B We might define female to be a binary variable taking on the value one for females and the value zero for males. The name in this case indicates the event with the value one. B The same information is captured by defining male to be one if the person is male and zero if the person is female. B Either of these is better than using gender because this name does not make it clear when the dummy variable is one: does gender = 1 correspond to male or female?
Lecture 18, ECON 123A, Fall 2011 18-5 Dale J. Poirier C Consider a wage example for which we have chosen the name female to indicate gender. B Further, we define a binary variable married to equal one if a person is married and zero if otherwise. B Table 7.1 gives a partial listing of a wage data set that might result. We see that Person 1 is female and not married, Person 2 is female and married, Person 3 is male and not married, and so on.

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Lecture 18, ECON 123A, Fall 2011 18-6 Dale J. Poirier
Lecture 18, ECON 123A, Fall 2011 18-7 Dale J. Poirier 7.2 A Single Dummy Independent Variable Consider the following simple model of hourly wage determination: 0 0 1 wage = \$ + * female + \$ educ + u . (7.1) C In model (7.1), only two observed factors affect wage : gender and education. Because female = 1 when the person is female, and female = 0 0 when the person is male, the parameter * has the following 0 interpretation: * is the difference in hourly wage between females and males, given the same amount of education (and the same error term u ). 0

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## This note was uploaded on 12/13/2011 for the course ECON 123a taught by Professor Staff during the Fall '08 term at UC Irvine.

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Lecture+18Multiple+Regression+Analysis+-+Dummies - Lecture...

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