plm (version 2.2-0)

pdim: Check for the Dimensions of the Panel

Description

This function checks the number of individuals and time observations in the panel and whether it is balanced or not.

Usage

pdim(x, ...)
# S3 method for default
pdim(x, y, ...)
# S3 method for data.frame
pdim(x, index = NULL, ...)
# S3 method for pdata.frame
pdim(x, ...)
# S3 method for pseries
pdim(x, ...)
# S3 method for panelmodel
pdim(x, ...)
# S3 method for pgmm
pdim(x, ...)
# S3 method for pdim
print(x, ...)

Arguments

x

a data.frame, a pdata.frame, a pseries, a panelmodel, or a pgmm object,

…

further arguments.

y

a vector,

index

see pdata.frame(),

Value

An object of class pdim containing the following elements:

nT

a list containing n, the number of individuals, T, the number of time observations, N the total number of observations,

Tint

a list containing two vectors (of type integer): Ti gives the number of observations for each individual and nt gives the number of individuals observed for each period,

balanced

a logical value: TRUE for a balanced panel, FALSE for an unbalanced panel,

panel.names

a list of character vectors: id.names contains the names of each individual and time.names contains the names of each period.

Details

pdim is called by the estimation functions and can be also used stand-alone.

See Also

is.pbalanced() to just determine balancedness of data (slightly faster than pdim), punbalancedness() for measures of unbalancedness, nobs(), pdata.frame(), pvar() to check for each variable if it varies cross-sectionally and over time.

Examples

# NOT RUN {
# There are 595 individuals
data("Wages", package = "plm")
pdim(Wages, 595)

# Gasoline contains two variables which are individual and time
# indexes and are the first two variables
data("Gasoline", package="plm")
pdim(Gasoline)

# Hedonic is an unbalanced panel, townid is the individual index
data("Hedonic", package = "plm")
pdim(Hedonic, "townid")

# An example of the panelmodel method
data("Produc", package = "plm")
z <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp,data=Produc,
         model="random", subset = gsp > 5000)
pdim(z)

# }