wmat.pdynmc: Extract Weighting Matrix of Fitted Model.

In pdynmc: Moment Condition Based Estimation of Linear Dynamic Panel Data Models

Extract Weighting Matrix of Fitted Model.

Description

wmat.pdynmc extracts weighting matrix from an object of class 'pdynmc'.

Usage

## S3 method for class 'pdynmc'
wmat(object, step = object$iter, ...)

Arguments

object	An object of class 'pdynmc'.
step	An integer denoting the iteration step for which fitted values are extracted (defaults to last iteration step used for obtaining parameter estimates).
...	further arguments.

Value

Extract weighting matrix from an object of class 'pdynmc'.

Author(s)

Markus Fritsch

See Also

pdynmc for fitting a linear dynamic panel data model.

Examples

## Load data
data(ABdata, package = "pdynmc")
dat <- ABdata
dat[,c(4:7)] <- log(dat[,c(4:7)])
dat <- dat[c(1:140), ]

## Code example
m1 <- pdynmc(dat = dat, varname.i = "firm", varname.t = "year",
    use.mc.diff = TRUE, use.mc.lev = FALSE, use.mc.nonlin = FALSE,
    include.y = TRUE, varname.y = "emp", lagTerms.y = 2,
    fur.con = TRUE, fur.con.diff = TRUE, fur.con.lev = FALSE,
    varname.reg.fur = c("wage", "capital", "output"), lagTerms.reg.fur = c(1,2,2),
    include.dum = TRUE, dum.diff = TRUE, dum.lev = FALSE, varname.dum = "year",
    w.mat = "iid.err", std.err = "corrected", estimation = "onestep",
    opt.meth = "none")
wmat(m1)


## Load data
 data(ABdata, package = "pdynmc")
 dat <- ABdata
 dat[,c(4:7)] <- log(dat[,c(4:7)])

 m1 <- pdynmc(dat = dat, varname.i = "firm", varname.t = "year",
    use.mc.diff = TRUE, use.mc.lev = FALSE, use.mc.nonlin = FALSE,
    include.y = TRUE, varname.y = "emp", lagTerms.y = 2,
    fur.con = TRUE, fur.con.diff = TRUE, fur.con.lev = FALSE,
    varname.reg.fur = c("wage", "capital", "output"), lagTerms.reg.fur = c(1,2,2),
    include.dum = TRUE, dum.diff = TRUE, dum.lev = FALSE, varname.dum = "year",
    w.mat = "iid.err", std.err = "corrected", estimation = "onestep",
    opt.meth = "none")
 wmat(m1)

pdynmc documentation built on Nov. 25, 2023, 1:08 a.m.