Lecture 9 logit probit
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Lecture 10: Logistical Regression II— Multinomial Data
www.columbia.eduLogit vs. Probit Review Use with a dichotomous dependent variable Need a link function F(Y) going from the original Y to continuous Y′ Probit: F(Y) = Φ-1(Y) Logit: F(Y) = log[Y/(1-Y)] Do the regression and transform the findings back from Y′to Y, interpreted as a probability
Lecture 9: Logit/Probit - Columbia University in the City ...
www.columbia.eduNonlinear Estimation In all these models Y, the dependent variable, was continuous. Independent variables could be dichotomous (dummy variables), but not the dependent var. This week we’ll start our exploration of non- linear estimation with dichotomous Y vars.
Lecture Notes on Propensity Score Matching
faculty.ndhu.edu.twLecture Notes on Propensity Score Matching Jin-Lung Lin This lecture note is intended solely for teaching. Some parts of the notes are taken from various
Chapter 2: Maximum Likelihood Estimation - Accueil
www.univ-orleans.frChapter 2: Maximum Likelihood Estimation Advanced Econometrics - HEC Lausanne Christophe Hurlin University of OrlØans December 9, 2013 Christophe Hurlin (University of OrlØans) Advanced Econometrics - HEC Lausanne December 9, 2013 1 / 207