plm (version 2.6-4)

make.pconsecutive: Make data consecutive (and, optionally, also balanced)

Description

This function makes the data consecutive for each individual (no "gaps" in time dimension per individual) and, optionally, also balanced

Usage

make.pconsecutive(x, ...)
# S3 method for data.frame
make.pconsecutive(x, balanced = FALSE, index = NULL, ...)
# S3 method for pdata.frame
make.pconsecutive(x, balanced = FALSE, ...)
# S3 method for pseries
make.pconsecutive(x, balanced = FALSE, ...)

Value

An object of the same class as the input x, i.e., a pdata.frame, data.frame or a pseries which is made time--consecutive based on the index variables. The returned data are sorted as a stacked time series.

Arguments

x

an object of class pdata.frame, data.frame, or pseries,

...

further arguments.

balanced

logical, indicating whether the data should additionally be made balanced (default: FALSE),

index

only relevant for data.frame interface; if NULL, the first two columns of the data.frame are assumed to be the index variables; if not NULL, both dimensions ('individual', 'time') need to be specified by index as character of length 2 for data frames, for further details see pdata.frame(),

Author

Kevin Tappe

Details

(p)data.frame and pseries objects are made consecutive, meaning their time periods are made consecutive per individual. For consecutiveness, the time dimension is interpreted to be numeric, and the data are extended to a regularly spaced sequence with distance 1 between the time periods for each individual (for each individual the time dimension become a sequence t, t+1, t+2, ..., where t is an integer). Non--index variables are filled with NA for the inserted elements (rows for (p)data.frames, vector elements for pseries).

With argument balanced = TRUE, additionally to be made consecutive, the data also can be made a balanced panel/pseries. Note: This means consecutive AND balanced; balancedness does not imply consecutiveness. In the result, each individual will have the same time periods in their time dimension by taking the min and max of the time index variable over all individuals (w/o NA values) and inserting the missing time periods. Looking at the number of rows of the resulting (pdata.frame) (elements for pseries), this results in nrow(make.pconsecutive(<.>, balanced = FALSE)) <= nrow(make.pconsecutive(<.>, balanced = TRUE)). For making the data only balanced, i.e., not demanding consecutiveness at the same time, use make.pbalanced() (see Examples for a comparison)).

Note: rows of (p)data.frames (elements for pseries) with NA values in individual or time index are not examined but silently dropped before the data are made consecutive. In this case, it is not clear which individual or time period is meant by the missing value(s). Especially, this means: If there are NA values in the first/last position of the original time periods for an individual, which usually depicts the beginning and ending of the time series for that individual, the beginning/end of the resulting time series is taken to be the min and max (w/o NA values) of the original time series for that individual, see also Examples. Thus, one might want to check if there are any NA values in the index variables before applying make.pconsecutive, and especially check for NA values in the first and last position for each individual in original data and, if so, maybe set those to some meaningful begin/end value for the time series.

See Also

is.pconsecutive() to check if data are consecutive; make.pbalanced() to make data only balanced (not consecutive).
punbalancedness() for two measures of unbalancedness, pdim() to check the dimensions of a 'pdata.frame' (and other objects), pvar() to check for individual and time variation of a 'pdata.frame' (and other objects), lag() for lagged (and leading) values of a 'pseries' object.
pseries(), data.frame(), pdata.frame().

Examples

# take data and make it non-consecutive
# by deletion of 2nd row (2nd time period for first individual)
data("Grunfeld", package = "plm")
nrow(Grunfeld)                             # 200 rows
Grunfeld_missing_period <- Grunfeld[-2, ]
is.pconsecutive(Grunfeld_missing_period)   # check for consecutiveness
make.pconsecutive(Grunfeld_missing_period) # make it consecutiveness


# argument balanced:
# First, make data non-consecutive and unbalanced
# by deletion of 2nd time period (year 1936) for all individuals
# and more time periods for first individual only
Grunfeld_unbalanced <- Grunfeld[Grunfeld$year != 1936, ]
Grunfeld_unbalanced <- Grunfeld_unbalanced[-c(1,4), ]
all(is.pconsecutive(Grunfeld_unbalanced)) # FALSE
pdim(Grunfeld_unbalanced)$balanced        # FALSE

g_consec_bal <- make.pconsecutive(Grunfeld_unbalanced, balanced = TRUE)
all(is.pconsecutive(g_consec_bal)) # TRUE
pdim(g_consec_bal)$balanced        # TRUE
nrow(g_consec_bal)                 # 200 rows
head(g_consec_bal)                 # 1st individual: years 1935, 1936, 1939 are NA

g_consec <- make.pconsecutive(Grunfeld_unbalanced) # default: balanced = FALSE
all(is.pconsecutive(g_consec)) # TRUE
pdim(g_consec)$balanced        # FALSE
nrow(g_consec)                 # 198 rows
head(g_consec)                 # 1st individual: years 1935, 1936 dropped, 1939 is NA 


# NA in 1st, 3rd time period (years 1935, 1937) for first individual
Grunfeld_NA <- Grunfeld
Grunfeld_NA[c(1, 3), "year"] <- NA
g_NA <- make.pconsecutive(Grunfeld_NA)
head(g_NA)        # 1936 is begin for 1st individual, 1937: NA for non-index vars
nrow(g_NA)        # 199, year 1935 from original data is dropped


# pdata.frame interface
pGrunfeld_missing_period <- pdata.frame(Grunfeld_missing_period)
make.pconsecutive(Grunfeld_missing_period)


# pseries interface
make.pconsecutive(pGrunfeld_missing_period$inv)


# comparison to make.pbalanced (makes the data only balanced, not consecutive)
g_bal <- make.pbalanced(Grunfeld_unbalanced)
all(is.pconsecutive(g_bal)) # FALSE
pdim(g_bal)$balanced        # TRUE
nrow(g_bal) # 190 rows