Premium
Bias of OLS Estimators due to Exclusion of Relevant Variables and Inclusion of Irrelevant Variables
Author(s) -
Basu Deepankar
Publication year - 2020
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/obes.12322
Subject(s) - omitted variable bias , estimator , instrumental variable , econometrics , variables , sign (mathematics) , statistics , interpretation (philosophy) , contrast (vision) , mathematics , regression , variable (mathematics) , inclusion (mineral) , economics , psychology , computer science , mathematical analysis , artificial intelligence , programming language , social psychology
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 bias 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 a running example of a simple wage regression to illustrate my arguments.