punbalancedness {plm} | R Documentation |
This function reports two unbalancedness measures for panel data as defined in Ahrens/Pincus (1981).
punbalancedness(x, ...) ## S3 method for class 'panelmodel' punbalancedness(x, ...) ## S3 method for class 'data.frame' punbalancedness(x, index = NULL, ...) ## S3 method for class 'pdata.frame' punbalancedness(x, ...)
x |
a |
index |
only relevant for |
... |
further arguments. |
punbalancedness
returns two measures for the unbalancedness of a
panel data set (called "gamma" (γ) and "nu" (ν)).
If the panel data are balanced, both measures equal 1. The more "unbalanced" the
panel data, the lower the measures (but > 0). The upper and lower bounds
as given in Ahrens/Pincus (1981) are:
0 < γ, ν ≤ 1, and for ν more precisely
1/n < ν ≤ 1, with n being the number of individuals (as in pdim(x)$nT$n
).
An application of the first measure ("gamma") is found in e. g. Baltagi/Song/Jung (2002), pp. 488-491, and Baltagi/Chang (1994), pp. 78–87, where it is used to measure the unbalancedness of various unbalanced data sets used for Monte Carlo simulation studies.
There exists also an extension of unbalancedness measures to nested panel
structures as developed in Baltagi/Song/Jung (2001), p. 368, but these are
not implemented in punbalancedness
as of now.
punbalancedness
uses output of pdim
to calculate the
unbalancedness measures, so inputs to punbalancedness
can be whatever
pdim
works on. pdim
returns a logical whether a panel data set
is balanced or not and detailed information about the number of individuals
and time observations (see pdim
).
A numeric vector containing two entries (in this order):
gamma |
unbalancedness measure "gamma" (γ) (as called by the Greek letter in Ahrens/Pincus (1981), p. 228), |
nu |
unbalancedness measure "nu" (ν) (as called by the Greek letter in Ahrens/Pincus (1981), p. 228). |
Calling punbalancedness
on an estimated panelmodel
object and
on the corresponding (p)data.frame
used for this estimation does not
necessarily yield the same result (true also for pdim
). When called on an estimated
panelmodel
, the number of observations (individual, time) actually used for
model estimation are taken into account. When called on a (p)data.frame
, the
rows in the (p)data.frame
are considered, disregarding any NA values in the
dependent or independent variable(s) which would be dropped during model estimation.
Kevin Tappe
Ahrens, H.; Pincus, R. (1981), “On two measures of unbalancedness in a one-way model and their relation to efficiency”, Biometrical Journal, 23(3), pp. 227–235.
Baltagi, Badi H.; Chang, Young-Jae (1994), “Incomplete panels: A comparative study of alternative estimators for the unbalanced one-way error component regression model”, Journal of Econometrics, 62(2), pp. 67–89.
Baltagi, Badi H.; Song, Seuck Heun; Jung, Byoung Cheol (2001), “The unbalanced nested error component regression model”, Journal of Econometrics, 101(2), pp. 357–381.
Baltagi, Badi H.; Song, Seuck H.; Jung, Byoung C. (2002), “A comparative study of alternative estimators for the unbalanced two-way error component regression model”, Econometrics Journal, 5(2), pp. 480–493.
# Grunfeld is a balanced panel, Hedonic is an unbalanced panel data(list=c("Grunfeld", "Hedonic"), package="plm") # Grunfeld has individual and time index in first two columns punbalancedness(Grunfeld) # c(1,1) indicates balanced panel pdim(Grunfeld)$balanced # TRUE # Hedonic has individual index in column "townid" (in last column) punbalancedness(Hedonic, index="townid") # c(0.472, 0.519) pdim(Hedonic, index="townid")$balanced # FALSE # punbalancedness on estimated models plm_mod_pool <- plm(inv ~ value + capital, data = Grunfeld) punbalancedness(plm_mod_pool) plm_mod_fe <- plm(inv ~ value + capital, data = Grunfeld[1:99, ], model = "within") punbalancedness(plm_mod_fe) # replicate results for panel data design no. 1 in Ahrens/Pincus (1981), p. 234 ind_d1 <- c(1,1,1,2,2,2,3,3,3,3,3,4,4,4,4,4,4,4,5,5,5,5,5,5,5) time_d1 <- c(1,2,3,1,2,3,1,2,3,4,5,1,2,3,4,5,6,7,1,2,3,4,5,6,7) df_d1 <- data.frame(individual = ind_d1, time = time_d1) punbalancedness(df_d1) # c(0.868, 0.887)