NEWS.md

In rbfmvar: Residual-Based Fully Modified Vector Autoregression

rbfmvar 2.0.0

Initial Release

• Implements Residual-Based Fully Modified VAR (RBFM-VAR) estimator following Chang (2000).

• Core estimation:

• rbfmvar(): Main estimation function for RBFM-VAR models.
• Handles unknown mixtures of I(0), I(1), and I(2) variables.

• FM+ bias correction for asymptotically valid inference.

• Lag selection:

• Automatic lag selection via AIC, BIC, or HQ information criteria.

• ic_table(): Display information criteria comparison.

• Long-run variance estimation:

• Bartlett (Newey-West), Parzen, and Quadratic Spectral kernels.

• Andrews (1991) automatic bandwidth selection.

• Inference:

• granger_test(): Granger non-causality testing with modified Wald statistics.
• granger_matrix(): Pairwise Granger causality tests.

• Asymptotically chi-squared inference regardless of integration orders.

• Impulse response analysis:

• irf(): Orthogonalized impulse response functions.

• Bootstrap confidence intervals via Kilian (1998) method.

• Forecast error variance decomposition:

• fevd(): Cholesky-identified variance decomposition.

• Forecasting:

• forecast(): Out-of-sample forecasting with prediction intervals.

• Methods:

• print(), summary(), plot() methods for all major objects.
• coef(), residuals(), fitted(), vcov() extractors.

References

Chang, Y. (2000). Vector Autoregressions with Unknown Mixtures of I(0), I(1), and I(2) Components. Econometric Theory, 16(6), 905-926. doi:10.1017/S0266466600166071

rbfmvar documentation built on April 9, 2026, 9:08 a.m.