Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/202952
Authors: 
Basu, Deepankar
Year of Publication: 
2018
Series/Report no.: 
Working Paper 2018-19
Abstract: 
In this paper I discuss three issues related to bias of OLS estimators in a general multivariate setting. First, I discuss the bias that arises from omitting relevant variables. I offer a geometric interpretation of such bias and derive sufficient conditions in terms of sign restrictions that allows us to determine the direction of bias. Second, I show that inclusion of some omitted variables will not necessarily reduce the magnitude of OVB as long as some others remain omitted. Third, I show that inclusion of irrelevant variables in a model with omitted variables can also have an impact on the bias of OLS estimators. I use the running example of a simple wage regression to illustrate my arguments.
Subjects: 
omitted variable
irrelevant variables
ordinary least squares
bias
JEL: 
C20
Document Type: 
Working Paper

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