pdynmc (version 0.9.6)

wmat.pdynmc: Extract Weighting Matrix of Fitted Model.

Description

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

Usage

# S3 method for 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`.

See Also

pdynmc for fitting a linear dynamic panel data model.

Examples

# NOT RUN {
## Load data from plm package
if(!requireNamespace("plm", quietly = TRUE)){
 stop("Dataset from package \"plm\" needed for this example.
 Please install the package.", call. = FALSE)
} else{
 data(EmplUK, package = "plm")
 dat <- EmplUK
 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)
}

# }
# NOT RUN {
## Load data from plm package
if(!requireNamespace("plm", quietly = TRUE)){
 stop("Dataset from package \"plm\" needed for this example.
 Please install the package.", call. = FALSE)
} else{
 data(EmplUK, package = "plm")
 dat <- EmplUK
 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)
}
# }
# NOT RUN {

# }