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Showing content from https://github.com/bsvars/bsvars/issues/84 below:

a model with t-distributed structural shocks · Issue #84 · bsvars/bsvars · GitHub

• develop the model
• estimate the degrees of freedom $\nu$ using the RAM algorithm by Vihola (2012) in the univariate version (assuming that the truncation is not essential).
• develop cpp function sample_lambda
• develop cpp function for df kernel
• update sample_lambda and log_kernel_df by standardising the $mode = \frac{s}{\nu + 2} = 1$ for the IG2 prior for $\lambda$ instead of the $mean = \frac{s}{\nu - 2}$ (simpler for numerical evaluation of the log_kernel_df)
• develop cpp function sample_df
• develop cpp function for the model
• specify_bsvar_t
• R method estimate for the model
• compute_* methods
• left to be done compute_variance_decompositions
• forecast method
• verify_() methods
• summary method
• documentation
• NEWS
• tests

• develop the method and the prior
• NOPE! No cpp function involved
• develop verify_autoregression.PosteriorBSVART
• change generic and methods verify_volatility() to verify_identification()
• develop verify_identification.BSVART
• include changes in the output class SDDRid* including the model for the *
• summary.SDDRid*
• documentation
• NEWS
• tests

• check forecast methods

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