Practical Regression: Discrete Dependent Variables Case Solution & Answer

This is the ninth in a series of lecture notes that, tied together in a manual, you might call “practical regression.” The purpose of these notes is to complement the theoretical content of most statistics texts practices based on nearly three decades of experience of the author, along with over a hundred years experience of colleagues who have offered advice tips. As the title of “practical regression” implies, these notes are a guide to regression in practice. This note addresses the issue of endogeneity, explaining how an explanatory variable may be endogenous (and thus its coefficient may be biased) if the cause is in doubt. Through an extensive learning curve example medicine, note introduces the concept of instrumental variables (IV) provides an intuitive explanation for why the tools to solve the problem of causality, and how to estimate IV and two stage least squares regressions. The paper describes the statistical evidence for the validity of the instruments.
by
David Dranove
Source: Kellogg School of Management
11 pages.
Release: June 11, 2012. Prod #: KEL643-PDF-ENG
Discrete dependent variables regression practical solution cases

Related Case Solutions:

Practical Regression: Time Series and Autocorrelation Practical Regression: Noise Heteroskedasticity and Grouped Data Practical Regression: Discrete Dependent Variables Modeling Discrete Choice: Categorical Dependent Variables, Logistic Regression, and Maximum Likelihood Estimation Practical Regression: Causality and Instrumental Variables Practical Regression: Causality and Instrumental Variables Practical Regression: Building Your Model: What Variables to Include Practical Regression: Regression Basics Practical Regression: Regression Basics Practical Regression: Log vs. Linear Specification