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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