Package: rbfmvar 2.1.0

rbfmvar: Residual-Based Fully Modified Vector Autoregression

Implements the Residual-Based Fully Modified Vector Autoregression (RBFM-VAR) estimator of Chang (2000) <doi:10.1017/S0266466600166058>. The RBFM-VAR procedure extends Phillips (1995) FM-VAR to handle any unknown mixture of I(0), I(1), and I(2) components without prior knowledge of the number or location of unit roots. Provides automatic lag selection via information criteria (AIC, BIC, HQ), long-run variance estimation using Bartlett, Parzen, or Quadratic Spectral kernels with Andrews (1991) <doi:10.2307/2938229> automatic bandwidth selection, Granger non-causality testing with asymptotically chi-squared Wald statistics, impulse response functions (IRF) with bootstrap confidence intervals, forecast error variance decomposition (FEVD), and out-of-sample forecasting.

Authors:Muhammad Alkhalaf [aut, cre, cph], Yoosoon Chang [ctb]

rbfmvar_2.1.0.tar.gz
rbfmvar_2.1.0.zip(r-4.7-any)rbfmvar_2.1.0.zip(r-4.6-any)rbfmvar_2.1.0.zip(r-4.5-any)
rbfmvar_2.1.0.tgz(r-4.6-any)rbfmvar_2.1.0.tgz(r-4.5-any)
rbfmvar_2.1.0.tar.gz(r-4.7-any)rbfmvar_2.1.0.tar.gz(r-4.6-any)
rbfmvar_2.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rbfmvar/json (API)

# Install 'rbfmvar' in R:
install.packages('rbfmvar', repos = c('https://muhammedalkhalaf.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/muhammedalkhalaf/rbfmvar/issues

On CRAN:

Conda:

3.40 score 1 stars 2 scripts 277 downloads 7 exports 1 dependencies

Last updated from:214b1af415. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK123
source / vignettesOK168
linux-releaseOK108
macos-releaseOK73
macos-oldrelOK102
windows-develOK55
windows-releaseOK70
windows-oldrelOK70
wasm-releaseOK91

Exports:fevdforecastgranger_matrixgranger_testic_tableirfrbfmvar

Dependencies:MASS