Bias of OLS Estimators due to Exclusion of Relevant Variables and Inclusion of Irrelevant Variables
在多元回归框架下，讨论了遗漏相关变量和包含无关变量如何导致OLS估计量偏误，并给出了偏误方向的符号条件，适合计量经济学研究者判断变量选择对估计结果的影响。
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.