Y 2 3 1 x note that the marginal eects for the two

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: as the reference category. –  Male: create a variable that equals 1 when male and 0 when female (female is the reference category) –  Race: create 2 variables (1 if black, 0 otherwise; and 1 if other, 0 otherwise) leaving white as the reference category –  Region: create variables (1 if west, 0 otherwise; 1 if northeast, 0 otherwise; and 1 if midwest, 0 otherwise) leaving south as the reference category Dummy Variables •  One way of addressing non- lineari:es is to separate a variable out into many dummy variables. –  E.g. We do not expect years of educa%on to have the same effect on income when going from 8 to 9 years as the effect going from 15 to 16 years. –  E.g When tes:ng for a dosage effect, you may want to know if going from 10mg to 20mg has the same effect as going from 90mg to 100mg. •  Dummy variables are interpreted as changing the intercept term for certain groups. yi = b1 + b2 xi + b3di + ei yi = (b1 + b3 ) + b2 xi + ei if di = 1 yi = b1 + b2 xi + ei if di = 0 9 2.5 Dummy Variables 2/17/12 Functional form: yi = 1 + 2 xi + 3 di + ui where the dummy variable di Graphically is either 1 or 0. y 2 3 1 x Note that the marginal e↵ects for the two groups (implicitly defined by the dummy variable) are equal but the constant terms di↵er. 2.6 Interac:on Terms Interaction Terms •  If Functional form:the slopes are different for different groups, when you put all of the groups together, you aren’t geong an accurate picture of the true yi = effect. 1 + 2 x1i + 3 x2i + 4 (x1i · x2i ) + ui –  In an extreme case, if the effect is posi:ve...
View Full Document

Ask a homework question - tutors are online