pdynmc (version 0.9.6)

nobs.pdynmc: Extract Number of Observations of Fitted Model.

Description

nobs.pdynmc extracts number of observations in cross-section dimension and longitudinal dimension of an object of class `pdynmc`.

Usage

# S3 method for pdynmc
nobs(object, ...)

Arguments

object

An object of class `pdynmc`.

...

further arguments.

Value

Extracts number of observations in cross-section dimension and longitudinal dimension of 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")
 nobs(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")
 nobs(m1)
}
# }
# NOT RUN {

# }