R: Summary method for objects of class varest, svarest and...

summary {vars}

R Documentation

Summary method for objects of class varest, svarest and svecest

Description

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

Usage

## 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), ...)

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()`.

Author(s)

Bernhard Pfaff

Examples

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)

[Package vars version 1.5-0 Index]