pdynmc (version 0.9.6)

optimIn.pdynmc: Extract Input Parameters of Numeric Optimization of Fitted Model.

Description

optimIn.pdynmc extracts input parameters of numeric optimization for an object of class `pdynmc`.

Usage

# S3 method for pdynmc
optimIn(object, step = object$iter, ...)

Arguments

object

An object of class `pdynmc`.

step

An integer denoting the iteration step for which input parameters are extracted (defaults to last iteration step used for obtaining parameter estimates).

...

further arguments.

Value

Extracts input parameters of numeric optimization from 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")
 optimIn(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)])

## Further 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 = "BFGS")
 optimIn(m1)
}
# }
# NOT RUN {

# }