EMET2007 Lecture 9 for Wattle

4 percentage points lecture 9 qualitative variables

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Unformatted text preview: 1, the expected house price increases by 5.4 percentage points ) Lecture 9 (qualitative variables) %∂price ∂ log (price ) = t 5.4% ∂colonial ∂colonial EMET2007/6007 8 st May 2013 18 / 53 Using dummy variables for multiple categories 1) De…ne membership in each category by a dummy variable 2) Leave out one category (which becomes the base category) For example, consider the categories: men and women, married and single. We have four groups: single women, married women, single men, married men We will set single men as the base category and include dummies for the other three Lecture 9 (qualitative variables) EMET2007/6007 8 st May 2013 19 / 53 \ log (wage ) = 3.21 (0.100 ) + 0.213 marrmale 0.198 marrfem (0.055 ) (0.0.058 ) + 0.079 educ + 0.027 exper (0.007 ) (0.005 ) + 0.029 tenure (0.007 ) n = 526 0.110 singfem (0.0.056 ) 2 0.00054exper (0.00011 ) 2 0.00053tenure (0.00023 ) 2 R = 0.364 Holding other things …xed, married women are expected to earn 19.8% less than single men (= the base category) Lecture 9 (qualitative variables) EMET2007/6007 8 st May 2013 20 / 53 Incorporating ordinal information using dummy variables Example: City credit ratings and municipal bond interest rates MBR = β0 + β1 CR + other factors MBR = Municipal bond rate CR = Credit rating from 0-4 (0=worst, 4=best so there are …ve categories) This speci…cation would probably not be appropriate as the credit rating only contains ordinal information. Lecture 9 (qualitative variables) EMET2007/6007 8 st May 2013 21 / 53 A better way to incorporate this information is to de…ne dummies: MBR = β0 + δ1 CR1 + δ2 CR2 + δ3 CR3 + δ4 CR4 + other factors Dummies indicating whether the particular rating applies, e.g. CR1=1 if CR=1 and CR1=0 otherwise. All e¤ects are measured in comparison to the worst rating (= base category). Lecture 9 (qualitative variables) EMET2007/6007 8 st May 2013 22 / 53 Interactions involving dummy variables Allowing for di¤erent slopes log (wage ) = β0 + δ0 female + β1 educ + δ1 female educ + ε β0 = intercept for men β1 = slope for men β0 + δ0 = intercept for women β1 + δ1 = slope for women Interesting hypotheses: The return to education is the same for men and women H0 : δ 1 = 0 The whole wage equation is the same for men and women H0 : δ0 = 0 and δ1 = 0 Lecture 9 (qualitative variables) EMET2007/6007 8 st May 2013 23 / 53 Interacting both the intercept and the slope with the female dummy enables one to model completely in...
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