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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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.