vars (version 1.5-3)

summary: Summary method for objects of class varest, svarest and svecest

Description

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

Usage

# S3 method for varest
summary(object, equations = NULL, ...)
# S3 method for varsum
print(x, digits = max(3, getOption("digits") - 3),
signif.stars = getOption("show.signif.stars"), ...)
# S3 method for svarest
summary(object,  ...)
# S3 method for svarsum
print(x, digits = max(3, getOption("digits") - 3), ...)
# S3 method for svecest
summary(object,  ...)
# S3 method for svecsum
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

object

Object of class ‘varest’, usually, a result of a call to VAR, or object of class ‘svarest’, usually, a result of a call to SVAR, or object of class ‘svecest’, usually, a result of a call to SVEC.

equations

Character vector of endogenous variable names for which summary results should be returned. The default is NULL and results are returned for all equations in the VAR.

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.

Value

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 VAR.

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 BQ.

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 optim().

iter

Integer, the count of iterations.

call

Call, the call to SVAR().

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 SVEC().

See Also

VAR, SVAR, SVEC

Examples

# NOT RUN {
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) 
# }