fgrowth is a S3 generic to compute (sequences of) suitably lagged / leaded and iterated growth rates, obtained with via the exact method of computation of through log differencing. By default growth rates are provided in percentage terms, but any scale factor can be applied. The growth operator G is a parsimonious wrapper around fgrowth, and also provides more flexibility when applied to data frames.
fgrowth(x, n = 1, diff = 1, …)G(x, n = 1, diff = 1, …)
# S3 method for default
fgrowth(x, n = 1, diff = 1, g = NULL, t = NULL, fill = NA,
logdiff = FALSE, scale = 100, power = 1, stubs = TRUE, …)
# S3 method for default
G(x, n = 1, diff = 1, g = NULL, t = NULL, fill = NA, logdiff = FALSE,
scale = 100, power = 1, stubs = TRUE, …)
# S3 method for matrix
fgrowth(x, n = 1, diff = 1, g = NULL, t = NULL, fill = NA,
logdiff = FALSE, scale = 100, power = 1,
stubs = length(n) + length(diff) > 2L, …)
# S3 method for matrix
G(x, n = 1, diff = 1, g = NULL, t = NULL, fill = NA, logdiff = FALSE,
scale = 100, power = 1, stubs = TRUE, …)
# S3 method for data.frame
fgrowth(x, n = 1, diff = 1, g = NULL, t = NULL, fill = NA,
logdiff = FALSE, scale = 100, power = 1,
stubs = length(n) + length(diff) > 2L, …)
# S3 method for data.frame
G(x, n = 1, diff = 1, by = NULL, t = NULL, cols = is.numeric,
fill = NA, logdiff = FALSE, scale = 100, power = 1, stubs = TRUE,
keep.ids = TRUE, …)
# Methods for compatibility with plm:
# S3 method for pseries
fgrowth(x, n = 1, diff = 1, fill = NA, logdiff = FALSE, scale = 100,
power = 1, stubs = TRUE, …)
# S3 method for pseries
G(x, n = 1, diff = 1, fill = NA, logdiff = FALSE, scale = 100,
power = 1, stubs = TRUE, …)
# S3 method for pdata.frame
fgrowth(x, n = 1, diff = 1, fill = NA, logdiff = FALSE, scale = 100,
power = 1, stubs = length(n) + length(diff) > 2L, …)
# S3 method for pdata.frame
G(x, n = 1, diff = 1, cols = is.numeric, fill = NA, logdiff = FALSE,
scale = 100, power = 1, stubs = TRUE, keep.ids = TRUE, …)
# Methods for grouped data frame / compatibility with dplyr:
# S3 method for grouped_df
fgrowth(x, n = 1, diff = 1, t = NULL, fill = NA, logdiff = FALSE,
scale = 100, power = 1, stubs = length(n) + length(diff) > 2L,
keep.ids = TRUE, …)
# S3 method for grouped_df
G(x, n = 1, diff = 1, t = NULL, fill = NA, logdiff = FALSE,
scale = 100, power = 1, stubs = TRUE, keep.ids = TRUE, …)
a numeric vector, matrix, data frame, panel series (plm::pseries), panel data frame (plm::pdata.frame) or grouped data frame (class 'grouped_df').
integer. A vector indicating the number of lags or leads.
integer. A vector of integers > 1 indicating the order of taking growth rates, e.g. diff = 2 means computing the growth rate of the growth rate.
data.frame method: Same as g, but also allows one- or two-sided formulas i.e. ~ group1 or var1 + var2 ~ group1 + group2. See Examples.
same input as g/by, to indicate the time-variable(s). For safe computation of growth rates on unordered time series and panels. Data Frame method also allows one-sided formula i.e. ~time. grouped_df method supports lazy-evaluation i.e. time (no quotes).
data.frame method: Select columns to compute growth rates using a function, column names, indices or a logical vector. Default: All numeric variables. Note: cols is ignored if a two-sided formula is passed to by.
value to insert when vectors are shifted. Default is NA.
logical. Compute log-difference growth rates instead of exact growth rates. See Details.
logical. Scale factor post-applied to growth rates, default is 100 which gives growth rates in percentage terms. See Details.
numeric. Apply a power to annualize or compound growth rates e.g. fgrowth(AirPassengers, 12, power = 1/12) is equivalent to ((AirPassengers/flag(AirPassengers, 12))^(1/12)-1)*100.
logical. TRUE will rename all computed columns by adding a prefix "LnGdiff." / "FnGdiff.", or "LnDlogdiff." / "FnDlogdiff." if logdiff = TRUE.
data.frame / pdata.frame / grouped_df methods: Logical. Drop all panel-identifiers from the output (which includes all variables passed to by or t). Note: For grouped / panel data frames identifiers are dropped, but the 'groups' / 'index' attributes are kept.
arguments to be passed to or from other methods.
x where the growth rate was taken diff times using lags n of itself, scaled by scale. Computations can be grouped by g/by and/or ordered by t. See Details and Examples.
fgrowth/G by default computes exact growth rates using repeat(diff) ((x[i]/x[i-n])^power - 1)*scale, and, if logdiff = TRUE approximate growth rates using repeat(diff) log(x[i]/x[i-n])*scale. So for diff > 1 it computes growth rate of growth rates etc.. For further details see the help pages for fdiff and flag.
flag/L/F, fdiff/D/Dlog, Time Series and Panel Series, Collapse Overview
# NOT RUN {
## Simple Time Series: AirPassengers
G(AirPassengers) # Growth rate, same as fgrowth(AirPassengers)
G(AirPassengers, logdiff = TRUE) # Log-difference
G(AirPassengers, 1, 2) # Growth rate of growth rate
G(AirPassengers, 12) # Seasonal growth rate (data is monthly)
head(G(AirPassengers, -2:2, 1:3)) # Sequence of leaded/lagged and iterated growth rates
# let's do some visual analysis
plot(G(AirPassengers, c(0, 1, 12)))
plot(stl(window(G(AirPassengers, 12), # Taking seasonal growth rate removes most seasonal variation
1950), "periodic"))
## Time Series Matrix of 4 EU Stock Market Indicators, recorded 260 days per year
plot(G(EuStockMarkets,c(0,260))) # Plot series and annual growth rates
summary(lm(L260G1.DAX ~., G(EuStockMarkets,260))) # Annual growth rate of DAX regressed on the
# growth rates of the other indicators
## World Development Panel Data
head(fgrowth(num_vars(wlddev), 1, 1, # Computes growth rates of numeric variables
wlddev$country, wlddev$year)) # fgrowth requires external inputs..
head(G(wlddev, 1, 1, ~country, ~year)) # Growth of numeric variables, id's attached
head(G(wlddev, 1, 1, ~country)) # Without t: Works because data is ordered
head(G(wlddev, 1, 1, PCGDP + LIFEEX ~ country, ~year)) # Growth of GDP per Capita & Life Expectancy
head(G(wlddev, 0:1, 1, ~ country, ~year, cols = 9:10)) # Same, also retaining original series
head(G(wlddev, 0:1, 1, ~ country, ~year, 9:10, # Dropping id columns
keep.ids = FALSE))
# Dynamic Panel Data Models:
summary(lm(G(PCGDP,1,1,iso3c,year) ~ # GDP growth regressed on it's lagged level
L(PCGDP,1,iso3c,year) + # and the growth rate of Life Expanctancy
G(LIFEEX,1,1,iso3c,year), data = wlddev))
g = qF(wlddev$country) # Omitting t and precomputing g allows for a
summary(lm(G(PCGDP,1,1,g) ~ L(PCGDP,1,g) + # bit more parsimonious specification
G(LIFEEX,1,1,g), wlddev))
summary(lm(G1.PCGDP ~., # Now adding level and lagged level of
L(G(wlddev,0:1,1, ~ country, ~year,9:10),0:1, # LIFEEX and lagged growth rates
~ country, ~year, keep.ids = FALSE)[-1]))
# }
# NOT RUN {
<!-- % No code relying on suggested package -->
## Using plm can make things easier, but avoid attaching or 'with' calls:
pwlddev <- plm::pdata.frame(wlddev, index = c("country","year"))
head(G(pwlddev, 0:1, 1, 9:10)) # Again growth rates of LIFEEX and PCGDP
PCGDP <- pwlddev$PCGDP # A panel-Series of GDP per Capita
head(G(PCGDP)) # Growth rate of the panel series
summary(lm(G1.PCGDP ~., # Running the dynamic model again ->
data = L(G(pwlddev,0:1,1,9:10),0:1, # code becomes a bit simpler
keep.ids = FALSE)[-1]))
# One could be tempted to also do something like this, but THIS DOES NOT WORK!!:
# -> a pseries is only created when subsetting the pdata.frame using $ or [[
summary(lm(G(PCGDP) ~ L(G(PCGDP,0:1)) + L(G(LIFEEX,0:1),0:1), pwlddev))
# To make it work, one needs to create pseries
LIFEEX <- pwlddev$LIFEEX
summary(lm(G(PCGDP) ~ L(G(PCGDP,0:1)) + L(G(LIFEEX,0:1),0:1))) # THIS WORKS !
## Using dplyr:
library(dplyr)
wlddev |> group_by(country) |>
select(PCGDP,LIFEEX) |> fgrowth(0:1) # Adding growth rates
wlddev |> group_by(country) |>
select(year,PCGDP,LIFEEX) |>
fgrowth(0:1, t = year) # Also using t (safer)
# }
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