analyze_ir.varlse {bvhar} | R Documentation |
Impulse Response Analysis
Description
Computes responses to impulses or orthogonal impulses
Usage
## S3 method for class 'varlse'
analyze_ir(
object,
lag_max = 10,
orthogonal = TRUE,
impulse_var,
response_var,
...
)
## S3 method for class 'vharlse'
analyze_ir(
object,
lag_max = 10,
orthogonal = TRUE,
impulse_var,
response_var,
...
)
## S3 method for class 'bvharirf'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
analyze_ir(object, lag_max, orthogonal, impulse_var, response_var, ...)
## S3 method for class 'bvharirf'
knit_print(x, ...)
Arguments
object |
Model object |
lag_max |
Maximum lag to investigate the impulse responses (By default, |
orthogonal |
Orthogonal impulses ( |
impulse_var |
Impulse variables character vector. If not specified, use every variable. |
response_var |
Response variables character vector. If not specified, use every variable. |
... |
not used |
x |
|
digits |
digit option to print |
Value
bvharirf
class
Responses to forecast errors
If orthogonal = FALSE
, the function gives W_j
VMA representation of the process such that
Y_t = \sum_{j = 0}^\infty W_j \epsilon_{t - j}
Responses to orthogonal impulses
If orthogonal = TRUE
, it gives orthogonalized VMA representation
\Theta
. Based on variance decomposition (Cholesky decomposition)
\Sigma = P P^T
where P
is lower triangular matrix,
impulse response analysis if performed under MA representation
y_t = \sum_{i = 0}^\infty \Theta_i v_{t - i}
Here,
\Theta_i = W_i P
and v_t = P^{-1} \epsilon_t
are orthogonal.
References
Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.