logit_cc: Functions for Estimating Interaction Effects in Logit and...

In davidaarmstrong/damisc: Dave Armstrong's Miscellaneous Functions

Functions for Estimating Interaction Effects in Logit and Probit Models

Description

Norton and Ai (2003) and Norton, Wang and Ai (2004) discuss methods for calculating the appropriate marginal effects for interactions in binary logit/probit models. These functions are direct translations of the Norton, Wang and Ai (2004) Stata code. These functions are not intended to be called by the user directly, rather they are called as needed by intEff.

Usage

logit_cc(obj = obj, int.var = int.var, vars = vars, b = b, X = X)

Arguments

obj	A binary logit or probit model estimated with glm.
int.var	The name of the interaction variable.
vars	A vector of the two variables involved in the interaction.
b	Coefficients from the glm object.
X	Model matrix from the glm object.

Value

A data frame with the following variable:

int_eff	The correctly calucalted marginal effect.
linear	The incorrectly calculated marginal effect following the linear model analogy.
phat	Predicted Pr(Y=1|X).
se_int_eff	Standard error of int_eff.
zstat	The interaction effect divided by its standard error

Author(s)

Dave Armstrong

References

Norton, Edward C., Hua Wang and Chunrong Ai. 2004. Computing Interaction Effects and Standard Errors in Logit and Probit Models. The Stata Journal 4(2): 154-167.

Ai, Chunrong and Edward C. Norton. 2003. Interaction Terms in Logit and Probit Models. Economics Letters 80(1): 123-129.

Norton, Edward C., Hua Wang and Chunrong Ai. 2004. inteff: Computing Interaction Effects and Standard Errors in Logit and Probit Models, Stata Code.

davidaarmstrong/damisc documentation built on Oct. 1, 2023, 3:05 p.m.