Abstract
The 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 non-linear probability models like 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 (2012) 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.
impact of confounding from rescaling when comparing conditional and
unconditional parameter estimates in non-linear probability models like 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 (2012) 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.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Sociological Methods & Research |
| ISSN | 0049-1241 |
| DOI | |
| Status | Udgivet - maj 2021 |