plm: punbalancedness – R documentation

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punbalancedness

Measures for Unbalancedness of Panel Data

Description

This function reports unbalancedness measures for panel data as defined in Ahrens and Pincus (1981) and Baltagi et al. (2001).

Usage

punbalancedness(x, ...)

## S3 method for class 'pdata.frame'
punbalancedness(x, ...)

## S3 method for class 'data.frame'
punbalancedness(x, index = NULL, ...)

## S3 method for class 'panelmodel'
punbalancedness(x, ...)

Arguments

`x`	a `panelmodel`, a `data.frame`, or a `pdata.frame` object,
`...`	further arguments.
`index`	only relevant for `data.frame` interface, for details see `pdata.frame()`,

Details

punbalancedness returns measures for the unbalancedness of a panel data set.

For two-dimensional data:
The two measures of Ahrens and Pincus (1981) are calculated, 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 and 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).

For nested panel data (meaning including a grouping variable):
The extension of the above measures by Baltagi et al. (2001), p. 368, are calculated:
- c1: measure of subgroup (individual) unbalancedness,
- c2: measure of time unbalancedness,
- c3: measure of group unbalancedness due to each group size.

Values are 1 if the data are balanced and become smaller as the data become more unbalanced.

An application of the measure "gamma" is found in e. g. Baltagi et al. (2001), pp. 488-491, and Baltagi and Chang (1994), pp. 78–87, where it is used to measure the unbalancedness of various unbalanced data sets used for Monte Carlo simulation studies. Measures c1, c2, c3 are used for similar purposes in Baltagi et al. (2001).

In the two-dimensional case, punbalancedness uses output of pdim() to calculate the two unbalancedness measures, so inputs to punbalancedness can be whatever pdim works on. pdim returns detailed information about the number of individuals and time observations (see pdim()).

Value

A named numeric containing either two or three entries, depending on the panel structure inputted:

For the two-dimensional panel structure, the entries are called gamma and nu,
For a nested panel structure, the entries are called c1, c2, c3.

Note

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.

Author(s)

Kevin Tappe

References

Ahrens H, Pincus R (1981). “On Two Measures of Unbalancedness in a One-Way Model and Their Relation to Efficiency.” Biometrical Journal, 23(3), 227–235. doi: 10.1002/bimj.4710230302, https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.4710230302.

Baltagi BH, Chang YJ (1994). “Incomplete panels: a comparative study of alternative estimators for the unbalanced one-way error component regression model.” Journal of Econometrics, 62, 67–89.

Baltagi BH, Song SH, Jung BC (2001). “The unbalanced nested error component regression model.” Journal of Econometrics, 101, 357–381.

Baltagi BH, Song SH, Jung BC (2002). “A comparative study of alternative estimators for the unbalanced two-way error component regression model.” The Econometrics Journal, 5(2), 480–493. ISSN 13684221, 1368423X.

Examples

# 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)

# example for a nested panel structure with a third index variable
# specifying a group (states are grouped by region) and without grouping
data("Produc", package = "plm")
punbalancedness(Produc, index = c("state", "year", "region"))
punbalancedness(Produc, index = c("state", "year"))

plm

Linear Models for Panel Data

v2.4-1

GPL (>= 2)

Authors

Yves Croissant [aut, cre], Giovanni Millo [aut], Kevin Tappe [aut], Ott Toomet [ctb], Christian Kleiber [ctb], Achim Zeileis [ctb], Arne Henningsen [ctb], Liviu Andronic [ctb], Nina Schoenfelder [ctb]

Initial release

2021-03-02