lag, lead, and diff functions for class pseries.
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an integer, the number of lags for the
further arguments (currently none evaluated).
the number of lags for the
This set of functions perform lagging, leading (lagging in the
opposite direction), and differencing operations on
objects, i. e., they take the panel structure of the data into
account by performing the operations per individual.
shift controls the shifting of observations to be used
shift = "time" (default): Methods respect the
numerical value in the time dimension of the index. The time
dimension needs to be interpretable as a sequence t, t+1, t+2,
... where t is an integer (from a technical viewpoint,
as.numeric(as.character(index(your_pdata.frame)[])) needs to
result in a meaningful integer).
shift = "row": Methods perform the shifting operation based
solely on the "physical position" of the observations,
i.e., neighbouring rows are shifted per individual. The value in the
time index is not relevant in this case.
For consecutive time periods per individual, a switch of shifting behaviour results in no difference. Different return values will occur for non-consecutive time periods per individual ("holes in time"), see also Examples.
An object of class
pseries, if the argument specifying the lag
has length 1 (argument
k in functions
lag in function
A matrix containing the various series in its columns, if the argument specifying the lag has length > 1.
The sign of
lag.pseries results in inverse behaviour
Yves Croissant and Kevin Tappe
To check if the time periods are consecutive per
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# First, create a pdata.frame data("EmplUK", package = "plm") Em <- pdata.frame(EmplUK) # Then extract a series, which becomes additionally a pseries z <- Em$output class(z) # compute the first and third lag, and the difference lagged twice lag(z) lag(z, 3) diff(z, 2) # compute negative lags (= leading values) lag(z, -1) lead(z, 1) # same as line above identical(lead(z, 1), lag(z, -1)) # TRUE # compute more than one lag and diff at once (matrix returned) lag(z, c(1,2)) diff(z, c(1,2)) ## demonstrate behaviour of shift = "time" vs. shift = "row" # delete 2nd time period for first individual (1978 is missing (not NA)): Em_hole <- Em[-2, ] is.pconsecutive(Em_hole) # check: non-consecutive for 1st individual now # original non-consecutive data: head(Em_hole$emp, 10) # for shift = "time", 1-1979 contains the value of former 1-1977 (2 periods lagged): head(lag(Em_hole$emp, k = 2, shift = "time"), 10) # for shift = "row", 1-1979 contains NA (2 rows lagged (and no entry for 1976): head(lag(Em_hole$emp, k = 2, shift = "row"), 10)
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