Package: rbfmvar 2.0.2

rbfmvar: Residual-Based Fully Modified Vector Autoregression

Implements the Residual-Based Fully Modified Vector Autoregression (RBFM-VAR) estimator of Chang (2000) <doi:10.1017/S0266466600166071>. 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_2.0.2.tar.gz
rbfmvar_2.0.2.zip(r-4.7)rbfmvar_2.0.2.zip(r-4.6)rbfmvar_2.0.2.zip(r-4.5)
rbfmvar_2.0.2.tgz(r-4.6-any)rbfmvar_2.0.2.tgz(r-4.5-any)
rbfmvar_2.0.2.tar.gz(r-4.7-any)rbfmvar_2.0.2.tar.gz(r-4.6-any)
rbfmvar_2.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
rbfmvar/json (API)
NEWS

# 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:

3.18 score 1 stars 2 scripts 453 downloads 7 exports 1 dependencies

Last updated from:cbd3b6fed4. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK116
source / vignettesOK150
linux-release-x86_64OK112
macos-release-arm64OK78
macos-oldrel-arm64OK83
windows-develOK87
windows-releaseOK66
windows-oldrelOK81
wasm-releaseOK112

Exports:fevdforecastgranger_matrixgranger_testic_tableirfrbfmvar

Dependencies:MASS