marginal {gmm}

Marginal effects Summary

Description

It produces the summary table of marginal effects for GLM estimation with GEL. Only implemented for ATEgel.

Usage

## S3 method for class 'ategel'
marginal(object, ...)

Arguments

object

An object of class ategel returned by the function ATEgel

...

Other arguments for other methods

Value

It returns a matrix with the marginal effects, the standard errors based on the Delta method when the link is nonlinear, the t-ratios, and the pvalues.

References

Owen, A.B. (2001), Empirical Likelihood. Monographs on Statistics and Applied Probability 92, Chapman and Hall/CRC

Examples

## We create some artificial data with unbalanced groups and binary outcome
genDat <- function(n)
    {
        eta=c(-1, .5, -.25, -.1)
        Z <- matrix(rnorm(n*4),ncol=4)
        b <- c(27.4, 13.7, 13.7, 13.7)
        bZ <- c(Z%*%b)
        Y1 <- as.numeric(rnorm(n, mean=210+bZ)>220)
        Y0 <- as.numeric(rnorm(n, mean=200-.5*bZ)>220)
        etaZ <- c(Z%*%eta)
        pZ <- exp(etaZ)/(1+exp(etaZ))
        T <- rbinom(n, 1, pZ)
        Y <- T*Y1+(1-T)*Y0
        X1 <- exp(Z[,1]/2)
        X2 <- Z[,2]/(1+exp(Z[,1]))
        X3 <- (Z[,1]*Z[,3]/25+0.6)^3
        X4 <- (Z[,2]+Z[,4]+20)^2
        data.frame(Y=Y, cbind(X1,X2,X3,X4), T=T)
    }

dat <- genDat(200)
res <- ATEgel(Y~T, ~X1+X2+X3+X4, data=dat, type="ET", family="logit")
summary(res)

marginal(res)