The VIF Score. What is it Good For? Absolutely Nothing
论文指出社会科学中常用的VIF阈值法（如VIF<10）不能排除多重共线性问题，反而可能掩盖遗漏变量导致的内生性偏误，建议研究者改用工具变量或只检验明确的理论假设。
Variance inflation factors (VIF scores) are regression diagnostics commonly invoked throughout the social sciences. Researchers typically take the perspective that VIF scores below a numerical rule-of-thumb threshold act as a “silver bullet” to dismiss any and all multicollinearity concerns. Yet, no valid logical basis exists for using VIF thresholds to reject the possibility of multicollinearity-induced type 1 errors. Reporting VIF scores below a threshold does not in any way add to the credibility of statistically significant results among correlated variables. In contrast to this “threshold perspective,” our analysis expands the scope of a perspective that has considered multicollinearity and misspecification. We demonstrate analytically that a regression omitting a relevant variable correlated with included variables that exhibit multicollinearity is susceptible to endogeneity-induced bias inflation and beta polarization, leading to the possible co-existence of type 1 errors and low VIF scores. Further, omitting variables explicitly reduces VIF scores. We conclude that the threshold perspective not only lacks any logical basis but also is fundamentally misleading as a rule-of-thumb. Instrumental variables represent one clear remedy for endogeneity-induced bias inflation. If exogenous instruments are unavailable, we encourage researchers to test only straightforward, unambiguous theory when using variables that exhibit multicollinearity, and to ensure that correlated co-variates exhibit the expected signs.