predict.bvar {BVAR}

Predict method for Bayesian VARs

Description

Retrieves / calculates forecasts for Bayesian VARs generated via bvar. If a forecast is already present and no settings are supplied it is simply retrieved, otherwise it will be calculated. To store the results you may want to assign the output using the setter function (predict(x) <- predict(x)). May also be used to update confidence bands.

Usage

## S3 method for class 'bvar'
predict(object, ..., conf_bands, n_thin = 1L, newdata)

predict(object) <- value

## S3 method for class 'bvar_fcast'
summary(object, vars = NULL, ...)

Arguments

object

A bvar object, obtained from bvar. Summary and print methods take in a bvar_fcast object.

...

A bv_fcast object or parameters to be fed into bv_fcast. Contains settings for the forecast.

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.

n_thin

Integer scalar. Every n_thin'th draw in object is used to predict, others are dropped.

newdata

Optional numeric matrix or dataframe. Used to base the prediction on.

value

A bvar_fcast object to assign.

vars

Optional numeric or character vector. Used to subset the summary to certain variables by position or name (must be available). Defaults to NULL, i.e. all variables.

Value

Returns a list of class bvar_fcast including forecasts at desired confidence bands. The summary method returns a numeric array of forecast paths at the specified confidence bands.

See Also

plot.bvar_fcast; bv_fcast

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)

# Calculate a forecast with an increased horizon
y <- predict(x, horizon = 20)

# Add some confidence bands and store the forecast
predict(x) <- predict(x, conf_bands = c(0.05, 0.16))

# Recalculate with different settings and increased thinning
predict(x, bv_fcast(24L), n_thin = 10L)

# Simulate some new data to predict on
predict(x, newdata = matrix(rnorm(300), ncol = 3))

# Calculate a conditional forecast (with a constrained second variable).
predict(x, cond_path = c(1, 1, 1, 1, 1, 1), cond_var = 2)

# Get a summary of the stored forecast
summary(x)

# Only get the summary for variable #2
summary(x, vars = 2L)