frmhet (version 1.1.3)

frmhet.pe: Fractional Regression Models under Unobserved Heterogeneity - Partial Effects

Description

frmhet.pe is used to compute average and/or conditional partial effects in fractional regression models under unobserved heterogeneity.

Usage

frmhet.pe(object, smearing = T, APE = T, CPE = F, at = NULL, which.x = NULL,
	table = T, variance = T)

Arguments

object

an object containing the results of an frmhet command.

smearing

a logical value indicating whether the smearing correction is to be applied

APE

a logical value indicating whether average partial effects are to be computed.

CPE

a logical value indicating whether conditional partial effects are to be computed.

at

a numeric vector containing the covariates' values at which the conditional partial effects are to be computed or the strings "mean" (the default) or "median", in which cases the covariates are evaluated at their mean or median values (or mode, in case of dummy variables), respectively.

which.x

a vector containing the names of the covariates to which the partial effects are to be computed.

table

a logical value indicating whether a summary table with the results should be printed.

variance

a logical value indicating whether the variance of the estimated partial effects should be calculated. Defaults to TRUE whenever table = TRUE.

Value

frmhet.pe returns a list with the following element:If variance = TRUE or table = TRUE, the previous list also contains the following element:When both average and conditional partial effects are requested, two lists containing the previous elements are returned, indexed by the prefixes ape and cpe.

Details

frmhet.pe calculates partial effects for fractional regression models estimated via frmhet. frmhet.pe may be used to compute average or conditional partial effects. These partial effects may be conditional only on observables, using the smearing estimator, or also on unobservables, setting the error term to zero. For calculating standard errors, it is taken into account the option that was previously chosen for estimating the model. See Ramalho and Ramalho (2016) for details on the computation of partial effects for fractional regression models under unobserved heterogeneity.

References

Ramalho, E.A. and J.J.S. Ramalho (2016), "Moment-based estimation of nonlinear regression models with boundary outcomes and endogeneity, with applications to nonnegative and fractional responses", Econometric Reviews, forthcoming (DOI: 10.1080/07474938.2014.976531).

See Also

frmhet, for fitting fractional regression models under unobserved heterogeneity. frmhet.reset, for the RESET test.

Examples

N <- 250
u <- rnorm(N)

X <- cbind(rnorm(N),rnorm(N))
dimnames(X)[[2]] <- c("X1","X2")

Z <- cbind(rnorm(N),rnorm(N),rnorm(N))
dimnames(Z)[[2]] <- c("Z1","Z2","Z3")

y <- exp(X[,1]+X[,2]+u)/(1+exp(X[,1]+X[,2]+u))

res <- frmhet(y,X,type="GMMx",table=FALSE)

#Smearing estimator of average partial effects for variable X1
frmhet.pe(res,which.x="X1")

#Naive estimator of conditional partial effects for all covariates,
#which are evaluated at X1=1 and X2=-1
frmhet.pe(res,smearing=FALSE,APE=FALSE,CPE=TRUE,at=c(1,-1))

## See the website http://evunix.uevora.pt/~jsr/FRM.htm for more examples.