predict {vars} | R Documentation |
Forecating a VAR object of class ‘varest
’ with
confidence bands.
## S3 method for class 'varest': predict(x, ..., n.ahead = 10, ci = 0.95, dumvar = NULL) ## S3 method for class 'vec2var': predict(x, ..., n.ahead = 10, ci = 0.95, dumvar = NULL)
x |
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 ’, if
the dumvar argument in ca.jo() has been used. The
matrix should have the same column dimension as in the call to
ca.jo() and row dimension equal to n.ahead . |
... |
Currently not used. |
The n.ahead
forecasts are computed recursively for the
estimated VAR, beginning with h = 1, 2, ..., n.ahead:
y_{T+1 | T} = A_1 y_T + ... + A_p y_{T+1-p} + C D_{T+1}
The variance-covariance matrix of the forecast errors is a function of Σ_u and Phi_s.
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 . |
Bernhard Pfaff
Hamilton, J. (1994), Time Series Analysis, Princeton University Press, Princeton.
Lütkepohl, H. (2006), New Introduction to Multiple Time Series Analysis, Springer, New York.
data(Canada) var.2c <- VAR(Canada, p = 2, type = "const") predict(var.2c, n.ahead = 8, ci = 0.95)