Consistent Monitoring of Stationarity and Cointegrating
Relationships
c(
person("Philipp", "Aschersleben", role = c("aut", "cre"),
email = "aschersleben@statistik.tu-dortmund.de"),
person("Martin", "Wagner", email = "wagner@statistik.tu-dortmund.de",
role = "aut", comment = "Author of underlying paper."),
person("Dominik", "Wied", email = "wied@statistik.tu-dortmund.de",
role = "aut", comment = "Author of underlying paper."))
We propose a consistent monitoring procedure to detect a
structural change from a cointegrating relationship to a spurious
relationship. The procedure is based on residuals from modified least
squares estimation, using either Fully Modified, Dynamic or Integrated
Modified OLS. It is inspired by Chu et al. (1996) <DOI:10.2307/2171955> in
that it is based on parameter estimation on a pre-break "calibration" period
only, rather than being based on sequential estimation over the full sample.
See the discussion paper <DOI:10.2139/ssrn.2624657> for further information.
This package provides the monitoring procedures for both the cointegration
and the stationarity case (while the latter is just a special case of the
former one) as well as printing and plotting methods for a clear
presentation of the results.
stats, graphics, matrixStats (>= 0.14.1)
Philipp Aschersleben [aut, cre],
Martin Wagner [aut] (Author of underlying paper.),
Dominik Wied [aut] (Author of underlying paper.)

Douglas Bates <bates@stat.wisc.edu>, David Brahm <brahm@alum.mit.edu>

This package tries to solve the memory restrictions posed
by large text files by breaking them into its columns, which
are subsequently read individually into R.

Read big text files column by column, sometimes much bigger than
available RAM, into R.
This package tries to solve the memory restrictions posed
by large text files by breaking them into their columns first.
These columns are later read individually into R. The package
also provides some helper functions for manipulating the newly
created objects.

This package tries to solve the memory restrictions posed by large text files by breaking them into their columns first. These columns are later read individually into R. The package also provides some helper functions for manipulating the newly created objects.