Description Usage Arguments Details Value Author(s) References See Also Examples
frmhet.pe
is used to compute average and/or conditional partial effects in fractional regression models
under unobserved heterogeneity.
1 2 
object 
an object containing the results of an 
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 
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

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.
frmhet.pe
returns a list with the following element:
PE.p 
a named vector of partial effects. 
If variance = TRUE
or table = TRUE
, the previous list also contains the following element:
PE.sd 
a named vector of standard errors of the estimated partial effects. 
When both average and conditional partial effects are requested, two lists containing the previous elements are
returned, indexed by the prefixes ape
and cpe
.
Joaquim J.S. Ramalho <jsr@uevora.pt>
Ramalho, E.A. and J.J.S. Ramalho (2016), "Momentbased 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).
frmhet
, for fitting fractional regression models under unobserved heterogeneity.
frmhet.reset
, for the RESET test.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  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.

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