predict: Predict method for objects of class varest and vec2var

In vars: VAR Modelling

Predict method for objects of class varest and vec2var

Description

Forecating a VAR object of class ‘varest’ or of class ‘vec2var’ with confidence bands.

Usage

## S3 method for class 'varest'
predict(object, ..., n.ahead = 10, ci = 0.95, dumvar = NULL)
## S3 method for class 'vec2var'
predict(object, ..., n.ahead = 10, ci = 0.95, dumvar = NULL)

Arguments

object	An object of class ‘varest’; generated by VAR(), or an object of class ‘vec2var’; generated by vec2var().
n.ahead	An integer specifying the number of forecast steps.
ci	The forecast confidence interval
dumvar	Matrix for objects of class ‘vec2var’ or ‘varest’, if the dumvar argument in ca.jo() has been used or if the exogen argument in VAR() has been used, respectively. The matrix should have the same column dimension as in the call to ca.jo() or to VAR() and row dimension equal to n.ahead.
...	Currently not used.

Details

The n.ahead forecasts are computed recursively for the estimated VAR, beginning with h = 1, 2, \ldots, n.ahead:

\bold{y}_{T+1 | T} = A_1 \bold{y}_T + \ldots + A_p \bold{y}_{T+1-p} + C D_{T+1}

The variance-covariance matrix of the forecast errors is a function of \Sigma_u and \Phi_s.

Value

A list with class attribute ‘varprd’ holding the following elements:

fcst	A list of matrices per endogenous variable containing the forecasted values with lower and upper bounds as well as the confidence interval.
endog	Matrix of the in-sample endogenous variables.
model	The estimated VAR object.
exo.fcst	If applicable provided values of exogenous variables, otherwise NULL.

Author(s)

Bernhard Pfaff

References

Hamilton, J. (1994), Time Series Analysis, Princeton University Press, Princeton.

Lütkepohl, H. (2006), New Introduction to Multiple Time Series Analysis, Springer, New York.

See Also

VAR, vec2var, plot, fanchart

Examples

data(Canada)
var.2c <- VAR(Canada, p = 2, type = "const")
predict(var.2c, n.ahead = 8, ci = 0.95) 

vars documentation built on June 24, 2024, 9:08 a.m.