ie_Slide07(1)

# ie_Slide07(1) - Introductory Econometrics ECON2206/ECON3209...

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Introductory Econometrics ECON2206/ECON3209 ie_Slides07 Rachida Ouysse y ie_Slides07 School of Economics, UNSW 1

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7. Multiple Regression Model: Qualitative Variables (Ch7) 7. Multiple Regression Model: Binary Variables • Lecture plan ualitative information and dummy (binary) variables Qualitative information and dummy (binary) variables – Regression with dummy regressors teractions with dummy gressors – Interactions with dummy regressors – Binary dependent variable: linear probability model ie_Slides07 School of Economics, UNSW 2
7. Multiple Regression Model: Qualitative Variables (Ch7) • Qualitative information – Many variables in social sciences are qualitative (non-numerical) factors that takes two values or can be organized into categories . – Consider the following examples: • Is gender important in determining expenditures on medical expenses? p • How would you model the impact of local crime on housing prices if crime rate were rated -none, moderate or high? • How do you include income as a determinant of cigarette demand when the data have only been collected by income class? • Has there been a shift over time in gaming revenues because of the introduction of smoking bans? • Did women delay giving births to get the Baby bonus? ie_Slides07 School of Economics, UNSW 3

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– In these examples, the explanatory variable is a categorical variable. It can be incorporated in the model with the use of a abe tca be copoa ted t e ode t t euseo dummy variables – They can be defined as binary (0-1) valued variables, known as dummy variables. eg. 1 for female, married, insured, etc. 0 for male, unmarried, uninsured, etc. – The assignment of values (0,1) is often determined by interpretation convenience. ie_Slides07 School of Economics, UNSW 4
7. Multiple Regression Model: Qualitative Variables (Ch7) • Dummy explanatory variables – We use dummy variables to incorporate qualitative information into regression models. eg. Wage model wage = β 0 + δ 0 female + β 1 educ + u , where δ 0 characterise e gender difference in wage the gender difference in wage. Under the ZCM assumption, E ( wage | female= 1 , educ ) g | , ) = β 0 + δ 0 + β 1 educ , E ( wage | female= 0 , educ ) = β 0 + β 1 educ , δ 0 represents an intercept shift . ie_Slides07 School of Economics, UNSW 5

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7. Multiple Regression Model: Qualitative Variables (Ch7) • Dummy explanatory variables – Interpretation of dummy eg. Wage model (continued) wage = β 0 + δ 0 female + β 1 educ + u . • Would you add the male dummy in the model? No, doing so leads to perfect collinearity (violation of MLR3). ales re treated as the ase group gainst which • Males are treated as the base group (against which comparisons are made). • We could regress wage on male and educ , where females would be base group and coefficient interpretation would be different.
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## This note was uploaded on 02/29/2012 for the course ECON 2206 taught by Professor Yang during the One '11 term at University of New South Wales.

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ie_Slide07(1) - Introductory Econometrics ECON2206/ECON3209...

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