bayesFAVAR_TVP: Estimates FAVAR-TVP model using MCMC sampler.
In GediminasB/bayesVAR_TVP: Bayesian analysis of Vector Autoregressive Models

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bayesFAVAR_TVP(Y, l = ncol(Y), n.factors, p = 1, nburn = 10000,
  nsim = 50000, tau = 40, beta.algorithm = "DK",
  reject.explosive = FALSE)

`Y`	Matrix or timeseries object
`l`	Number of time series to vectorize; all ts to vectorize must come before all other series
`n.factors`	Number of factors tu use for first l series
`p`	Integer for the lag order (default is p=1)
`nburn`	Number of MCMC draws used to initialize the sampler (defaults to 10000)
`nsim`	Number of MCMC draws excluding burn-in (defaults to 50000)
`tau`	Length of training sample used for determining prior parameters via OLS
`beta.algorithm`	Algorithm for drawing time-varying VAR parameters. Either 'DK' for Durbin and Coopman 2002, or 'CC' for Carter and Cohn 1994. Defaults to 'DK'
`reject.explosive`	Should the explosive (not stable) draws of VAR parameters be rejected? Defaults to 'FALSE'. For rationale consult Cogley and Sargent 2005. May significantly increase computtion time!

`beta`	Draws of time-varying parameters (beta_t). 4D array [t x M x M*p+1 x draw]
`H`	Draws of observation equation error term covariance matrix H. 3D array [M x M x draw]
`Q`	Draws of state equation error term covariance matrix Q. 3D array [M x M x draw]
`L`	Draws of \Lambda. 3D array [n.factors x l x draw]
`F`	Draws of factors. 3D array
`R`	Draws of observation equation error term covariance matrix R.