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fgrowth

Fast Growth Rates for Time Series and Panel Data

Description

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.

Usage

fgrowth(x, n = 1, diff = 1, ...)

G(x, n = 1, diff = 1, ...)

## Default S3 method:
fgrowth(x, n = 1, diff = 1, g = NULL, t = NULL, fill = NA,
        logdiff = FALSE, scale = 100, power = 1, stubs = TRUE, ...)
## Default S3 method:
G(x, n = 1, diff = 1, g = NULL, t = NULL, fill = NA, logdiff = FALSE,
  scale = 100, power = 1, stubs = TRUE, ...)

## S3 method for class '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 class 'matrix'
G(x, n = 1, diff = 1, g = NULL, t = NULL, fill = NA, logdiff = FALSE,
  scale = 100, power = 1, stubs = TRUE, ...)

## S3 method for class '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 class '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 class 'pseries'
fgrowth(x, n = 1, diff = 1, fill = NA, logdiff = FALSE, scale = 100,
        power = 1, stubs = TRUE, ...)
## S3 method for class 'pseries'
G(x, n = 1, diff = 1, fill = NA, logdiff = FALSE, scale = 100,
  power = 1, stubs = TRUE, ...)

## S3 method for class '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 class '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 class '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 class 'grouped_df'
G(x, n = 1, diff = 1, t = NULL, fill = NA, logdiff = FALSE,
  scale = 100, power = 1, stubs = TRUE, keep.ids = TRUE, ...)

Arguments

`x`	a numeric vector, matrix, data frame, panel series (`plm::pseries`), panel data frame (`plm::pdata.frame`) or grouped data frame (class 'grouped_df').
`n`	integer. A vector indicating the number of lags or leads.
`diff`	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.
`g`	a factor, `GRP` object, atomic vector (internally converted to factor) or a list of vectors / factors (internally converted to a `GRP` object) used to group `x`.
`by`	data.frame method: Same as `g`, but also allows one- or two-sided formulas i.e. `~ group1` or `var1 + var2 ~ group1 + group2`. See Examples.
`t`	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).
`cols`	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`.
`fill`	value to insert when vectors are shifted. Default is `NA`.
`logdiff`	logical. Compute log-difference growth rates instead of exact growth rates. See Details.
`scale`	logical. Scale factor post-applied to growth rates, default is 100 which gives growth rates in percentage terms. See Details.
`power`	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`.
`stubs`	logical. `TRUE` will rename all computed columns by adding a prefix "L`n`G`diff`." / "F`n`G`diff`.", or "L`n`Dlog`diff`." / "F`n`Dlog`diff`." if `logdiff = TRUE`.
`keep.ids`	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.

Details

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.

Value

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.

Examples

## 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]))
 
## 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)

collapse

Advanced and Fast Data Transformation

v1.5.3

GPL (>= 2) | file LICENSE

Authors

Sebastian Krantz [aut, cre], Matt Dowle [ctb], Arun Srinivasan [ctb], Laurent Berge [ctb], Dirk Eddelbuettel [ctb], Josh Pasek [ctb], Kevin Tappe [ctb], R Core Team and contributors worldwide [ctb], Martyn Plummer [cph], 1999-2016 The R Core Team [cph]

Initial release

2021-03-05