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psacf

Auto- and Cross- Covariance and Correlation Function Estimation for Panel Series

Description

psacf, pspacf and psccf compute (and by default plot) estimates of the auto-, partial auto- and cross- correlation or covariance functions for panel-vectors and plm::pseries. They are analogues to acf, pacf and ccf.

Usage

psacf(x, ...)
pspacf(x, ...)
psccf(x, y, ...)

## Default S3 method:
psacf(x, g, t = NULL, lag.max = NULL, type = c("correlation", "covariance","partial"),
      plot = TRUE, gscale = TRUE, ...)
## Default S3 method:
pspacf(x, g, t = NULL, lag.max = NULL, plot = TRUE, gscale = TRUE, ...)
## Default S3 method:
psccf(x, y, g, t = NULL, lag.max = NULL, type = c("correlation", "covariance"),
      plot = TRUE, gscale = TRUE, ...)

## S3 method for class 'pseries'
psacf(x, lag.max = NULL, type = c("correlation", "covariance","partial"),
      plot = TRUE, gscale = TRUE, ...)
## S3 method for class 'pseries'
pspacf(x, lag.max = NULL, plot = TRUE, gscale = TRUE, ...)
## S3 method for class 'pseries'
psccf(x, y, lag.max = NULL, type = c("correlation", "covariance"),
      plot = TRUE, gscale = TRUE, ...)

## S3 method for class 'data.frame'
psacf(x, by, t = NULL, cols = is.numeric, lag.max = NULL,
      type = c("correlation", "covariance","partial"), plot = TRUE, gscale = TRUE, ...)
## S3 method for class 'data.frame'
pspacf(x, by, t = NULL, cols = is.numeric, lag.max = NULL,
       plot = TRUE, gscale = TRUE, ...)

 ## S3 method for class 'pdata.frame'
psacf(x, cols = is.numeric, lag.max = NULL,
      type = c("correlation", "covariance","partial"), plot = TRUE, gscale = TRUE, ...)
## S3 method for class 'pdata.frame'
pspacf(x, cols = is.numeric, lag.max = NULL, plot = TRUE, gscale = TRUE, ...)

Arguments

`x, y`	a numeric vector, panel series (`plm::pseries`), data frame or panel data-frame (`plm::pdata.frame`).
`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, y`.
`by`	data.frame method: Same input as `g`, but also allows one- or two-sided formulas using the variables in `x`, i.e. `~ idvar` or `var1 + var2 ~ idvar1 + idvar2`.
`t`	same input as g, to indicate the time-variable(s). For secure computations on unordered panel-vectors. Data frame method also allows one-sided formula i.e. `~time`.
`cols`	data.frame method: Select columns using a function, column names, indices or a logical vector. Note: `cols` is ignored if a two-sided formula is passed to `by`.
`lag.max`	integer. Maximum lag at which to calculate the acf. Default is `2*sqrt(length(x)/ng)` where `ng` is the number of groups in the panel series / supplied to `g`.
`type`	character. String giving the type of acf to be computed. Allowed values are "correlation" (the default), "covariance" or "partial".
`plot`	logical. If `TRUE` (default) the acf is plotted.
`gscale`	logical. Do a groupwise scaling / standardization of `x, y` (using `fscale` and the groups supplied to `g`) before computing panel-autocovariances / correlations. See Details.
`...`	further arguments to be passed to `plot.acf`.

Details

If gscale = TRUE data are standardized within each group (using fscale) such that the group-mean is 0 and the group-standard deviation is 1. This is strongly recommended for most panels to get rid of individual-specific heterogeneity which would corrupt the ACF computations.

After scaling, psacf, pspacf and psccf compute the ACF/CCF by creating a matrix of panel-lags of the series using flag and then correlating this matrix with the series (x, y) using cor and pairwise-complete observations. This may require a lot of memory on large data, but is done because passing a sequence of lags to flag and thus calling flag and cor one time is much faster than calling them lag.max times. The partial ACF is computed from the ACF using a Yule-Walker decomposition, in the same way as in pacf.

Value

An object of class 'acf', see acf. The result is returned invisibly if plot = TRUE.

Note

For plm::pseries and plm::pdata.frame, the first index variable is assumed to be the group-id and the second the time variable. If more than 2 index variables are attached to plm::pseries, the last one is taken as the time variable and the others are taken as group-id's and interacted.

The pdata.frame method only works for properly subsetted objects of class 'pdata.frame'. A list of 'pseries' will not work.

Examples

## World Development Panel Data
head(wlddev)                                                    # See also help(wlddev)
psacf(wlddev$PCGDP, wlddev$country, wlddev$year)                # ACF of GDP per Capita
psacf(wlddev, PCGDP ~ country, ~year)                           # Same using data.frame method
psacf(wlddev$PCGDP, wlddev$country)                             # The Data is sorted, can omit t
pspacf(wlddev$PCGDP, wlddev$country)                            # Partial ACF
psccf(wlddev$PCGDP, wlddev$LIFEEX, wlddev$country)              # CCF with Life-Expectancy at Birth

psacf(wlddev, PCGDP + LIFEEX + ODA ~ country, ~year)            # ACF and CCF of GDP, LIFEEX and ODA
psacf(wlddev, ~ country, ~year, c(9:10,12))                     # Same, using cols argument
pspacf(wlddev, ~ country, ~year, c(9:10,12))                    # Partial ACF
 
## Using plm:
pwlddev <- plm::pdata.frame(wlddev, index = c("country","year"))# Creating a Panel Data Frame
PCGDP <- pwlddev$PCGDP                                          # Panel Series  of GDP per Capita
LIFEEX <- pwlddev$LIFEEX                                        # Panel Series  of Life Expectancy
psacf(PCGDP)                                                    # Same as above, more parsimonious
pspacf(PCGDP)
psccf(PCGDP, LIFEEX)
psacf(pwlddev[c(9:10,12)])
pspacf(pwlddev[c(9:10,12)])

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