R: Predict method for objects of class varest and vec2var

predict {vars}

R Documentation

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, …, n.ahead:

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

The variance-covariance matrix of the forecast errors is a function of Σ_u and Φ_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.

Examples

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

[Package vars version 1.5-0 Index]