Use determinant(vcov, logarithm = TRUE)$modulus for
numerical stability
Suggest xts package for enhanced time series support
tempdisagg 1.1.1
minor changes
Fix tests for R devel
tempdisagg 1.1 (2023-03-08)
minor changes
Use GHA instead of Travis
bug fixes
Adjustment to work with latest version of tsbox
documentation
?td: uniform uses criterion = “additive”
tempdisagg 1.0 (2020-02-07)
major changes
works now with most time series classes, as supported by the tsbox
package.
disagregation is possible to all frequencies (e.g., monthly to
daily). Disaggregation takes into account the calendar, e.g., the fact
that February is shorter than other months. (#30)
new method: “fast”, a shortcut for chow-lin-fixed with fixed.rho =
0.99999. The method returns approximately the same results as
“denton-cholette”, but is much faster. (#14)
new vignettes: intro to tempdisagg, disaggregation to high
frequency
under the hood
supports three modes: tsbox, ts, numeric
markdown in roxygen, NEWS.md
testthat infrastructure
tempdisagg 0.25 (2016-07-10)
changes visible to the user
new methods: “dynamic-maxlog”, “dynamic-minrss”, “dynamic-fixed”, as
described in Santos Silva and Cardoso, 2001. Many thanks to Tommaso Di
Fonzo for providing a blueprint written in GAUSS.
updated documentation to include new methods.
minor changes
better checks for non-time-series inputs.
(https://github.com/christophsax/tempdisagg/issues/20)
added extensive numerical testing on travis.
bug fixes
ta() returns correct results if conversion is “last” or “first”, and
the first or the last period is incomplete.
(https://github.com/christophsax/tempdisagg/issues/22)
tempdisagg 0.24 (2014-12-07)
changes visible to the user:
retropolation: ‘td’ will performs both extra- and retropolation if
the high frequency series covers a larger time span than the low
frequency series.
low frequency values are ignored if series is longer than high
frequency series (with a warning).
suggestion to use ‘denton-cholette’ when the original ‘denton’
method is chosen.
tempdisagg 0.23 (2014-01-11)
changes visible to the user
Our R-Journal article on temporal disaggregation explains tempdisagg
in more detail. Links are included in the package description, the help
files and the README file.
minor changes
warning in ta() if a time series contains internal NAs.
formating tweaks in the help files.
tempdisagg 0.22 (2013-08-07)
changes visible to the user
predict method for ‘td’ is now different from fitted:
$fitted.values of a ‘td’ object now containts the low-frequency
fitted values of a regression or the low-frequency indicator in case of
the Denton methods. The values are be accessed by fitted().
The final high frequency series is now stored in $values. As before,
these values are accessed by predict().
Package overview (?tempdisagg)
Demo (demo(tempdisagg))
argument ‘truncated.rho = 0’ instead of ‘no.neg = TRUE’. This allows
for truncation values different from 0. Default behavior is the same as
in 0.21.
bug fixes
in 0.21, ta() produced an error if less than a low-frequency unit
was covered by high frequency data. Now it produces series containing
only NA.
If a singular data matrix is entered, there is a new warning.
tempdisagg 0.21 (2013-01-21)
changes visible to the user
new methods available: “chow-lin-fixed” and “litterman-fixed”. Using
the “fixed.rho” argument, an autoregressive parameter may be specified
by the user.
interface changes: “chow-lin-maxlog-ecotrim” and
“chow-lin-maxlog-quilis” are defined as new methods. No need for the old
‘vcov’ argument anymore.
new defaults: method = “chow-lin-maxlog”, neg.rho = FALSE with
positive values for rho only, the chow-lin-maxlog method generally
outperforms the other methods.
all relevant arguments are directly entered to td()
summary output: If neg.rho = FALSE and a negative rho is truncated
to 0, and indicator is shown in the summary output.
non time-series mode: optionally, standard vectors can be used
instead of time series. In this case, the frequency of low frequency
variable is 1, while the fraction of the high frequency variable is
specified by the ‘to’ argument
updated help files
under the hood
td() is rewritten and has a clear structure now.
GLS Regressions are performed by the new CalcGLS() function, which
uses QR-decomposition instead of matrix-inversion. This is faster and
numerically stable. It resolves an issue wher large (or small) numbers
have led to a ‘system is computationally singular’ error.