Case Number: 7-112-004, Year Published: 2012
HBS Number: KEL638
Economics, Market Research, Statistical Methods
This is the fourth in a series of lecture notes which, if tied together into a textbook, might be entitled “Practical Regression.” The purpose of the notes is to supplement the theoretical content of most statistics texts with practical advice based on nearly three decades of experience of the author, combined with over one hundred years of experience of colleagues who have offered guidance. As the title “Practical Regression” suggests, these notes are a guide to performing regression in practice. This technical note introduces the concept of endogeneity bias with specific coverage of omitted variable bias. Students who read this note will understand why omitting key predictors can sometimes bias the coefficients of included variables. The note illustrates omitted variable bias by means of an extended example and offers practical advice for model building that balances the desire for parsimony developed in a previous note with the need to limit omitted variable bias.
Students will learn the following: -The definition of endogeneity bias -Why omitted variables can create endogeneity bias -How to think about the potential for omitted variable bias and determine, if appropriate data are available, whether a model has suffered from this bias -How to build a regression model that balances the desire for parsimony with the need to limit omitted variable bias
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