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cointmonitoR

Consistent Monitoring of Stationarity and Cointegrating Relationships

https://github.com/aschersleben/cointmonitor

Science Score: 23.0%

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    Found 14 DOI reference(s) in README
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    Low similarity (12.0%) to scientific vocabulary
Last synced: 11 months ago · JSON representation

Repository

Consistent Monitoring of Stationarity and Cointegrating Relationships

Basic Info
  • Host: GitHub
  • Owner: aschersleben
  • Language: R
  • Default Branch: master
  • Size: 74.2 KB
Statistics
  • Stars: 3
  • Watchers: 1
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Created over 10 years ago · Last pushed about 10 years ago
Metadata Files
Readme

README.md

cointmonitoR

Consistent Monitoring of Stationarity and Cointegrating Relationships.

Travis-CI Build Status CRAN Status Badge

  • Installation (including latest CRAN version of cointReg) r install.packages("cointmonitoR") library("cointmonitoR")

  • Simple example (stationarity, structural break): r set.seed(1909) eps <- rnorm(200) x <- c(eps[1:100], cumsum(eps[101:200]) / 2) test <- monitorStationarity(x, m = 93) print(test) oldpar <- par(mfrow = c(2, 1)) plot(test, what = "both", legend = FALSE) par(oldpar)

  • Package vignette: Provides further examples and explanations. r vignette("cointmonitoR")

  • Package help page: Overview of all available functions: r package?cointmonitoR

  • Package issues

  • Package NEWS

  • Installation of the current development version from GitHub: r devtools::install_github("aschersleben/cointmonitoR", build_vignettes = TRUE)

Theoretical background

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) 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 for further information.

This package provides the monitoring procedures for both the cointegration and the stationarity case (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.

References

  • Aschersleben, P. and M. Wagner (2016). cointReg: Parameter Estimation and Inference in a Cointegrating Regression. R package version 0.2.0. https://CRAN.R-project.org/package=cointReg

  • Chu, C.J., M. Stinchcombe and H. White (1996): "Monitoring Structural Change", Econometrica, 64, 1045--1065, DOI:10.2307/2297912.

  • Wagner, M. and D. Wied (2015): "Monitoring Stationarity and Cointegration," Discussion Paper, DOI:10.2139/ssrn.2624657

GitHub Events

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Last synced: over 3 years ago

All Time
  • Total Commits: 11
  • Total Committers: 1
  • Avg Commits per committer: 11.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
aschersleben a****n@s****e 11
Committer Domains (Top 20 + Academic)
statistik.tu-dortmund.de: 1

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Last synced: 12 months ago

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  • Total issues: 0
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  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
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Packages

  • Total packages: 1
  • Total downloads:
    • cran 223 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 1
  • Total maintainers: 1
cran.r-project.org: cointmonitoR

Consistent Monitoring of Stationarity and Cointegrating Relationships

  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 223 Last month
Rankings
Stargazers count: 28.5%
Forks count: 28.8%
Dependent packages count: 29.8%
Dependent repos count: 35.5%
Average: 36.9%
Downloads: 61.9%
Maintainers (1)
aschersleben@statistik.tu-dortmund.de
Last synced: 12 months ago

Dependencies

DESCRIPTION cran
  • cointReg >= 0.2.0 depends
  • graphics * imports
  • matrixStats >= 0.14.1 imports
  • stats * imports
  • knitr * suggests
  • rmarkdown * suggests