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

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

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 class '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'.

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")
optimIn(m1)


## Load data
 data(ABdata, package = "pdynmc")
 dat <- ABdata
 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)

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