Description Usage Arguments Value Author(s) See Also Examples

'summary' methods for class '"varest"', '"svarest"' and '"svecest"'.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## S3 method for class 'varest'
summary(object, equations = NULL, ...)
## S3 method for class 'varsum'
print(x, digits = max(3, getOption("digits") - 3),
signif.stars = getOption("show.signif.stars"), ...)
## S3 method for class 'svarest'
summary(object, ...)
## S3 method for class 'svarsum'
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'svecest'
summary(object, ...)
## S3 method for class 'svecsum'
print(x, digits = max(3, getOption("digits") - 3), ...)
``` |

`object` |
Object of class ‘ |

`equations` |
Character vector of endogenous variable names for
which summary results should be returned. The default is |

`x` |
Object with class attribute ‘varsum’, ‘svarsum’. |

`digits` |
the number of significant digits to use when printing. |

`signif.stars` |
logical. If 'TRUE', ‘significance stars’ are printed for each coefficient. |

`...` |
further arguments passed to or from other methods. |

Returns either a list with class attribute `varsum`

which contains the
following elements:

`names` |
Character vector with the names of the endogenous correlation matrix of VAR residuals. |

`logLik` |
Numeric, value of log Likelihood. |

`obs` |
Integer, sample size. |

`roots` |
Vector, roots of the characteristic polynomial. |

`type` |
Character vector, deterministic regressors included in VAR: |

`call` |
Call, the initial call to |

Or a list with class attribute `svarsum`

which contains the
following elements:

`type` |
Character, the type of SVAR-model. |

`A` |
Matrix, estimated coefficients for A matrix. |

`B` |
Matrix, estimated coefficients for B matrix. |

`Ase` |
Matrix, standard errors for A matrix. |

`Bse` |
Matrix, standard errors for B matrix. |

`LRIM` |
Matrix, long-run impact coefficients for |

`Sigma.U` |
Matrix, variance/covariance of reduced form residuals. |

`logLik` |
Numeric, value of log-Likelihood. |

`LR` |
htest, LR result of over-identification test. |

`obs` |
Integer, number of observations used. |

`opt` |
List, result of |

`iter` |
Integer, the count of iterations. |

`call` |
Call, the call to |

Or a list with class attribute `svecsum`

which contains the
following elements:

`type` |
Character, the type of SVEC-model. |

`SR` |
Matrix, contemporaneous impact matrix. |

`LR` |
Matrix, long-run impact matrix. |

`SRse` |
Matrix, standard errors for SR matrix. |

`LRse` |
Matrix, standard errors for LR matrix. |

`Sigma.U` |
Matrix, variance/covariance of reduced form residuals. |

`logLik` |
Numeric, value of log-Likelihood. |

`LRover` |
htest, LR result of over-identification test. |

`obs` |
Integer, number of observations used. |

`r` |
Integer, co-integration rank of VECM. |

`iter` |
Integer, the count of iterations. |

`call` |
Call, the call to |

Bernhard Pfaff

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
data(Canada)
## summary-method for varest
var.2c <- VAR(Canada, p = 2 , type = "const")
summary(var.2c)
## summary-method for svarest
amat <- diag(4)
diag(amat) <- NA
amat[2, 1] <- NA
amat[4, 1] <- NA
## Estimation method scoring
svar.a <- SVAR(x = var.2c, estmethod = "scoring", Amat = amat, Bmat = NULL,
max.iter = 100, maxls = 1000, conv.crit = 1.0e-8)
summary(svar.a)
## summary-method for svecest
vecm <- ca.jo(Canada[, c("prod", "e", "U", "rw")], type = "trace",
ecdet = "trend", K = 3, spec = "transitory")
SR <- matrix(NA, nrow = 4, ncol = 4)
SR[4, 2] <- 0
LR <- matrix(NA, nrow = 4, ncol = 4)
LR[1, 2:4] <- 0
LR[2:4, 4] <- 0
svec.b <- SVEC(vecm, LR = LR, SR = SR, r = 1, lrtest = FALSE, boot =
FALSE)
summary(svec.b)
``` |

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