heteroscedasticity that depends on an index different from that underlying the mean-responseʺ. We show that such (multiplicative …) heteroscedasticity, whose form is not parametrically specified, effectively induces exclusion restrictions on the outcomes equation. The …

sheds light on the connection between both approaches. I provide alternative formulas (approximations) for the bias and …-robust standard errors in RD settings. Bias mitigation approaches are discussed and a novel, bootstrap higher-order bias correction …

effect having an opposite sign from the true effect. We present an expression for the asymptotic bias of both OLS and IV … estimators and discuss the conditions under which sign reversal may occur. We provide a method for eliminating this bias when …

This paper introduces bias-corrected estimators for nonlinear panel data models with both time invariant and time …/selection bias. We then estimate the primary equation by fixed effects including an appropriately constructed control function from … both steps might employ nonlinear fixed effects procedures it is necessary to bias adjust the estimates due to the …

This paper provides an expression for the bias of the OLS estimator of the schooling coefficient in a simple static … coefficient is biased upward, and the bias is increasing with potential labor-market experience and the degree of earnings … persistence. In addition, NLSY data are used to show that the magnitude of the persistence bias is non-negligible, and the bias …

This paper uses the control function to develop a framework for testing for selection bias. The idea behind our … magnitude of potential selection bias. Averaging this correction term with respect to appropriate weights yields the degree of … selection bias for alternative treatment effects of interest. One advantage of our framework is that it motivates when is …