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.

Version:	2.0.2
Depends:	R (≥ 3.5.0)
Imports:	stats, MASS
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown
Published:	2026-04-09
DOI:	10.32614/CRAN.package.rbfmvar
Author:	Muhammad Alkhalaf [aut, cre, cph], Yoosoon Chang [ctb] (Original RBFM-VAR methodology)
Maintainer:	Muhammad Alkhalaf <muhammedalkhalaf at gmail.com>
BugReports:	https://github.com/muhammedalkhalaf/rbfmvar/issues
License:	GPL-3
URL:	https://github.com/muhammedalkhalaf/rbfmvar
NeedsCompilation:	no
Materials:	README, NEWS
CRAN checks:	rbfmvar results

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