Methods to compute linear h-step ahead prediction coefficients based on localised and iterated Yule-Walker estimates and empirical mean squared prediction errors for the resulting predictors. Also, functions to compute autocovariances for AR(p) processes, to simulate tvARMA(p,q) time series, and to verify an assumption from Kley et al. (2017), Preprint arXiv:1611.04460 <http://arxiv.org/abs/1611.04460>.

Install the latest version of this package by entering the following in R:

`install.packages("forecastSNSTS")`

Author | Tobias Kley [aut, cre], Philip Preuss [aut], Piotr Fryzlewicz [aut] |

Date of publication | 2017-01-20 10:48:10 |

Maintainer | Tobias Kley <t.kley@lse.ac.uk> |

License | GPL (>= 2) |

Version | 1.1-1 |

http://github.com/tobiaskley/forecastSNSTS |

**acfARp:** Compute autocovariances of an AR(p) process

**f:** Compute f(delta) for a tvAR(p) process

**forecastSNSTS-package:** Forecasting of Stationary and Non-Stationary Time Series

**MSPE:** Mean squared h-step ahead prediction errors

**plot.MSPE:** Plot a 'MSPE' object

**predCoef:** h-step Prediction coefficients

**ts-models-tvARMA:** Simulation of an tvARMA(p) time series.

inst

inst/examples

inst/examples/mspe.R
inst/examples/computeMSPE.R
inst/examples/tvARMA.R
tests

tests/testthat.R
tests/testthat

tests/testthat/test-tvARMA.R
tests/testthat/test-MSPE.R
tests/testthat/test-computeMSPE.R
tests/testthat/test-f.R
tests/testthat/test-acfARp.R
tests/testthat/test-coefficients.R
src

src/computeMSPE.cpp

src/predCoef.cpp

src/tvARMA.cpp

src/RcppExports.cpp

NAMESPACE

demo

demo/tvARMA11.R
demo/00Index

NEWS

R

R/models.R
R/acfARp.R
R/forecastSNSTS-package.R
R/mspe.R
R/RcppExports.R
R/f.R
MD5

DESCRIPTION

man

man/MSPE.Rd
man/forecastSNSTS-package.Rd
man/ts-models-tvARMA.Rd
man/predCoef.Rd
man/plot.MSPE.Rd
man/acfARp.Rd
man/f.Rd
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