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.

### 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)

[Package vars version 1.5-3 Index]