Package: starvars 1.1.10
Andrea Bucci
starvars: Vector Logistic Smooth Transition Models Estimation and Prediction
Allows the user to estimate a vector logistic smooth transition autoregressive model via maximum log-likelihood or nonlinear least squares. It further permits to test for linearity in the multivariate framework against a vector logistic smooth transition autoregressive model with a single transition variable. The estimation method is discussed in Terasvirta and Yang (2014, <doi:10.1108/S0731-9053(2013)0000031008>). Also, realized covariances can be constructed from stock market prices or returns, as explained in Andersen et al. (2001, <doi:10.1016/S0304-405X(01)00055-1>).
Authors:
starvars_1.1.10.tar.gz
starvars_1.1.10.zip(r-4.5)starvars_1.1.10.zip(r-4.4)starvars_1.1.10.zip(r-4.3)
starvars_1.1.10.tgz(r-4.5-any)starvars_1.1.10.tgz(r-4.4-any)starvars_1.1.10.tgz(r-4.3-any)
starvars_1.1.10.tar.gz(r-4.5-noble)starvars_1.1.10.tar.gz(r-4.4-noble)
starvars_1.1.10.tgz(r-4.4-emscripten)starvars_1.1.10.tgz(r-4.3-emscripten)
starvars.pdf |starvars.html✨
starvars/json (API)
| # Install 'starvars' in R: |
| install.packages('starvars', repos = c('https://andbucci.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/andbucci/starvars/issues
- Realized - Monthly time series used to test VLSTAR models.
- Sample5minutes - Ten simulated prices series for 19 trading days in January 2010.
- techprices - Daily closing prices of 3 tech stocks.
Last updated 3 years agofrom:c4b8fcb3bc. Checks:1 OK, 7 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 30 2025 |
| R-4.5-win | NOTE | Jan 30 2025 |
| R-4.5-mac | NOTE | Jan 30 2025 |
| R-4.5-linux | NOTE | Jan 30 2025 |
| R-4.4-win | NOTE | Jan 30 2025 |
| R-4.4-mac | NOTE | Jan 30 2025 |
| R-4.3-win | NOTE | Jan 30 2025 |
| R-4.3-mac | NOTE | Jan 30 2025 |
Exports:logLik.VLSTARlrvarbartmultiCUMSUMrcovstartingVLSTARVLSTARVLSTARjoint
Dependencies:base64encbslibcachemclicodetoolscolorspacecommonmarkcrayoncurldeldirDEoptimRdigestdoSNOWellipseevaluatefastmapFNNfontawesomeforeachfsgluehighrhtmltoolshttpuvinterpiteratorsjpegjquerylibjsonlitekernlabKernSmoothknitrkslaterlatticelatticeExtraleapslessRlifecyclelmtestmagrittrMASSMatrixmatrixcalcmclustmemoisemgcvmimemulticoolmvtnormnlmeopenxlsxoptimParallelpngpracmapromisesquantmodR6rappdirsRColorBrewerRcppRcppEigenrlangrobustbasesandwichsassshinysnowsourcetoolsstringistrucchangeTTRurcavarswithrxfunxtablextsyamlzipzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Coefficient method for objects of class VLSTAR | coef coef.VLSTAR coefficients |
| Log-Likelihood method | logLik logLik.VLSTAR |
| Long-run variance using Bartlett kernel | lrvarbart |
| Multivariate CUMSUM test | multiCUMSUM print.multiCUMSUM |
| Plot methods for a VLSTAR object | plot.VLSTAR |
| Plot methods for a vlstarpred object | plot.vlstarpred |
| VLSTAR Prediction | predict predict.VLSTAR print.vlstarpred |
| Print method for objects of class VLSTAR | print print.VLSTAR |
| Realized Covariance | rcov |
| Monthly time series used to test VLSTAR models. | Realized |
| Ten simulated prices series for 19 trading days in January 2010. | Sample5minutes |
| Starting parameters for a VLSTAR model | startingVLSTAR |
| Summary method for objects of class VLSTAR | print.summary print.summary.VLSTAR summary summary.VLSTAR |
| Daily closing prices of 3 tech stocks. | techprices |
| VLSTAR- Estimation | VLSTAR |
| Joint linearity test | print.VLSTARjoint VLSTARjoint |