A note on a reformulation of the KHB method

The KHB method has rapidly become popular as a way of separating theimpact of confounding from rescaling when comparing conditional andunconditional parameter estimates in non-linear probability models like the logitand probit. In this note we show that the same estimates can be obtained in asomewhat different way to that advanced by Karlson, Holm, and Breen (2012) intheir original article and implemented in the user-written Stata command khb.While the KHB method and this revised KHB method both work by holdingconstant the residual variance of the model, the revised method makescomparisons across multiple nested models easier than the original method. The Karlson–Holm–Breen (KHB) method has rapidly become popular as a way of separating the impact of confounding from rescaling when comparing conditional and unconditional parameter estimates in nonlinear probability models such as the logit and probit. In this note, we show that the same estimates can be obtained in a somewhat different way to that advanced by Karlson, Holm, and Breen in their original article and implemented in the user-written Stata command khb. While the KHB method and this revised KHB method both work by holding constant the residual variance of the model, the revised method makes comparisons across multiple nested models easier than the original method.