Regression with a Binary Dependent Variable - Chapter 9

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Regressionwitha Binary DependentVariableChapter9MichaelAshCPPAL ecture22CourseNotesIEndgameITake-home nalIDistributedFriday 19 MayIDueTuesday 23 May (Paper or emailedPDFok; no Word,Excel,etc.)IProblemSet7IOptional,wo rth up to 2 percentagepointsof extracreditIDueFriday 19 MayIRegressionwitha Binary DependentVariableBinary DependentVariablesIOutcomecanbe coded1 or 0 (yes or no,approvedor denied,successor failure)Examples?IInterprettheregression as modelingtheprobability thatthedependentvariableequalsone(Y= 1).IRecallthatfor a Binary Variable ,E(Y) = Pr(Y= 1)HMDA exampleIOutcome:loandenialis coded1, loanapproval0IKeyexplanatory Variable :blackIOtherexplanatory variables:P=I, credithistory, LTV, Probability model (LPM)Yi= 0+ 1X1i+ 2X2i+ + kXki+ 1expressesthechangein probability thatY= 1 associatedwitha ^Yiexpressestheprobability thatYi= 1Pr(Y= 1jX1;X2; : : : ;Xk) = 0+ 1X1+ 2X2+ + kXk=^YShortcomingsof theLPMI\NonconformingPredictedProb

Ordered logit or probit. I Discrete Choice Data, e.g., mode of travel. Characteristics of choice, chooser, and interaction. Multinomial logit or probit, I Can sometimes convert to several binary problems. I Censored and Truncated Regression Models. Tobit or sample selection models.

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