fitted.bvar: Fitted and residual methods for Bayesian VARs

In BVAR: Hierarchical Bayesian Vector Autoregression

Fitted and residual methods for Bayesian VARs

Description

Calculates fitted or residual values for Bayesian VAR models generated with bvar.

Usage

## S3 method for class 'bvar'
fitted(object, type = c("quantile", "mean"), conf_bands = 0.5, ...)

## S3 method for class 'bvar'
residuals(object, type = c("quantile", "mean"), conf_bands = 0.5, ...)

## S3 method for class 'bvar_resid'
plot(x, vars = NULL, mar = c(2, 2, 2, 0.5), ...)

Arguments

object	A bvar object, obtained from bvar.
type	Character scalar. Whether to return quantile or mean values. Note that conf_bands is ignored for mean values.
conf_bands	Numeric vector of confidence bands to apply. E.g. for bands at 5%, 10%, 90% and 95% set this to c(0.05, 0.1). Note that the median, i.e. 0.5 is always included.
...	Not used.
x	Object of class bvar_fitted / bvar_resid.
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 NULL, i.e. all hyperparameters.
mar	Numeric vector. Margins for par.

Value

Returns a numeric array of class bvar_fitted or bvar_resid at the specified values.

See Also

bvar

Examples

# 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 a BVAR using one lag, default settings and very few draws
x <- bvar(data, lags = 1, n_draw = 1000L, n_burn = 200L, verbose = FALSE)

# Get fitted values and adjust confidence bands to 10%, 50% and 90%
fitted(x, conf_bands = 0.10)

# Get the residuals of variable 1
resid(x, vars = 1)

## Not run: 
# Get residuals and plot them
plot(residuals(x))

## End(Not run)

BVAR documentation built on May 29, 2024, 5:34 a.m.