coda {BVAR} | R Documentation |
Methods for coda Markov chain Monte Carlo objects
Description
Methods to convert parameter and/or coefficient draws from bvar
to coda's mcmc
(or mcmc.list
)
format for further processing.
Usage
as.mcmc.bvar(
x,
vars = NULL,
vars_response = NULL,
vars_impulse = NULL,
chains = list(),
...
)
as.mcmc.bvar_chains(
x,
vars = NULL,
vars_response = NULL,
vars_impulse = NULL,
chains = list(),
...
)
Arguments
x |
A |
vars |
Character vector used to select variables. Elements are matched
to hyperparameters or coefficients. Coefficients may be matched based on
the dependent variable (by providing the name or position) or the
explanatory variables (by providing the name and the desired lag). See the
example section for a demonstration. Defaults to |
vars_response , vars_impulse |
Optional character or integer vectors used to select coefficents. Dependent variables are specified with vars_response, explanatory ones with vars_impulse. See the example section for a demonstration. |
chains |
List with additional |
... |
Other parameters for |
Value
Returns a coda mcmc
(or
mcmc.list
) object.
See Also
Examples
library("coda")
# Access a subset of the fred_qd dataset
data <- fred_qd[, c("CPIAUCSL", "UNRATE", "FEDFUNDS")]
# Transform it to be stationary
data <- fred_transform(data, codes = c(5, 5, 1), lag = 4)
# Estimate two BVARs using one lag, default settings and very few draws
x <- bvar(data, lags = 1, n_draw = 750L, n_burn = 250L, verbose = FALSE)
y <- bvar(data, lags = 1, n_draw = 750L, n_burn = 250L, verbose = FALSE)
# Convert the hyperparameter lambda
as.mcmc(x, vars = c("lambda"))
# Convert coefficients for the first dependent, use chains in method
as.mcmc(structure(list(x, y), class = "bvar_chains"), vars = "CPIAUCSL")
# Convert the coefs of variable three's first lag, use in the generic
as.mcmc(x, vars = "FEDFUNDS-lag1", chains = y)
# Convert hyperparameters and constant coefficient values for variable 1
as.mcmc(x, vars = c("lambda", "CPI", "constant"))
# Specify coefficent values to convert in alternative way
as.mcmc(x, vars_impulse = c("FED", "CPI"), vars_response = "UNRATE")