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)
|
Loading required package: MASS
Loading required package: strucchange
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
Loading required package: urca
Loading required package: lmtest
VAR Estimation Results:
=========================
Endogenous variables: e, prod, rw, U
Deterministic variables: const
Sample size: 82
Log Likelihood: -175.819
Roots of the characteristic polynomial:
0.995 0.9081 0.9081 0.7381 0.7381 0.1856 0.1429 0.1429
Call:
VAR(y = Canada, p = 2, type = "const")
Estimation results for equation e:
==================================
e = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + const
Estimate Std. Error t value Pr(>|t|)
e.l1 1.638e+00 1.500e-01 10.918 < 2e-16 ***
prod.l1 1.673e-01 6.114e-02 2.736 0.00780 **
rw.l1 -6.312e-02 5.524e-02 -1.143 0.25692
U.l1 2.656e-01 2.028e-01 1.310 0.19444
e.l2 -4.971e-01 1.595e-01 -3.116 0.00262 **
prod.l2 -1.017e-01 6.607e-02 -1.539 0.12824
rw.l2 3.844e-03 5.552e-02 0.069 0.94499
U.l2 1.327e-01 2.073e-01 0.640 0.52418
const -1.370e+02 5.585e+01 -2.453 0.01655 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3628 on 73 degrees of freedom
Multiple R-Squared: 0.9985, Adjusted R-squared: 0.9984
F-statistic: 6189 on 8 and 73 DF, p-value: < 2.2e-16
Estimation results for equation prod:
=====================================
prod = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + const
Estimate Std. Error t value Pr(>|t|)
e.l1 -0.17277 0.26977 -0.640 0.52390
prod.l1 1.15043 0.10995 10.464 3.57e-16 ***
rw.l1 0.05130 0.09934 0.516 0.60710
U.l1 -0.47850 0.36470 -1.312 0.19362
e.l2 0.38526 0.28688 1.343 0.18346
prod.l2 -0.17241 0.11881 -1.451 0.15104
rw.l2 -0.11885 0.09985 -1.190 0.23778
U.l2 1.01592 0.37285 2.725 0.00805 **
const -166.77552 100.43388 -1.661 0.10109
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6525 on 73 degrees of freedom
Multiple R-Squared: 0.9787, Adjusted R-squared: 0.9764
F-statistic: 419.3 on 8 and 73 DF, p-value: < 2.2e-16
Estimation results for equation rw:
===================================
rw = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + const
Estimate Std. Error t value Pr(>|t|)
e.l1 -0.268833 0.322619 -0.833 0.407
prod.l1 -0.081065 0.131487 -0.617 0.539
rw.l1 0.895478 0.118800 7.538 1.04e-10 ***
U.l1 0.012130 0.436149 0.028 0.978
e.l2 0.367849 0.343087 1.072 0.287
prod.l2 -0.005181 0.142093 -0.036 0.971
rw.l2 0.052677 0.119410 0.441 0.660
U.l2 -0.127708 0.445892 -0.286 0.775
const -33.188339 120.110525 -0.276 0.783
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7803 on 73 degrees of freedom
Multiple R-Squared: 0.9989, Adjusted R-squared: 0.9987
F-statistic: 8009 on 8 and 73 DF, p-value: < 2.2e-16
Estimation results for equation U:
==================================
U = e.l1 + prod.l1 + rw.l1 + U.l1 + e.l2 + prod.l2 + rw.l2 + U.l2 + const
Estimate Std. Error t value Pr(>|t|)
e.l1 -0.58076 0.11563 -5.023 3.49e-06 ***
prod.l1 -0.07812 0.04713 -1.658 0.101682
rw.l1 0.01866 0.04258 0.438 0.662463
U.l1 0.61893 0.15632 3.959 0.000173 ***
e.l2 0.40982 0.12296 3.333 0.001352 **
prod.l2 0.05212 0.05093 1.023 0.309513
rw.l2 0.04180 0.04280 0.977 0.331928
U.l2 -0.07117 0.15981 -0.445 0.657395
const 149.78056 43.04810 3.479 0.000851 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2797 on 73 degrees of freedom
Multiple R-Squared: 0.9726, Adjusted R-squared: 0.9696
F-statistic: 324 on 8 and 73 DF, p-value: < 2.2e-16
Covariance matrix of residuals:
e prod rw U
e 0.131635 -0.007469 -0.04210 -0.06909
prod -0.007469 0.425711 0.06461 0.01392
rw -0.042099 0.064613 0.60886 0.03422
U -0.069087 0.013923 0.03422 0.07821
Correlation matrix of residuals:
e prod rw U
e 1.00000 -0.03155 -0.1487 -0.6809
prod -0.03155 1.00000 0.1269 0.0763
rw -0.14870 0.12691 1.0000 0.1568
U -0.68090 0.07630 0.1568 1.0000
SVAR Estimation Results:
========================
Call:
SVAR(x = var.2c, estmethod = "scoring", Amat = amat, Bmat = NULL,
max.iter = 100, conv.crit = 1e-08, maxls = 1000)
Type: A-model
Sample size: 82
Log Likelihood: -196.855
Method: scoring
Number of iterations: 14
LR overidentification test:
LR overidentification
data: Canada
Chi^2 = 3.9, df = 4, p-value = 0.4
Estimated A matrix:
e prod rw U
e 2.756 0.000 0.000 0.000
prod 0.087 1.533 0.000 0.000
rw 0.000 0.000 1.282 0.000
U 2.562 0.000 0.000 4.882
Estimated standard errors for A matrix:
e prod rw U
e 0.2152 0.0000 0.0000 0.0000
prod 0.3044 0.1197 0.0000 0.0000
rw 0.0000 0.0000 0.1001 0.0000
U 0.3643 0.0000 0.0000 0.3813
Estimated B matrix:
e prod rw U
e 1 0 0 0
prod 0 1 0 0
rw 0 0 1 0
U 0 0 0 1
Covariance matrix of reduced form residuals (*100):
e prod rw U
e 13.1635 -0.7469 0.00 -6.909
prod -0.7469 42.5711 0.00 0.392
rw 0.0000 0.0000 60.89 0.000
U -6.9087 0.3920 0.00 7.821
SVEC Estimation Results:
========================
Call:
SVEC(x = vecm, LR = LR, SR = SR, r = 1, lrtest = FALSE, boot = FALSE)
Type: B-model
Sample size: 81
Log Likelihood: -161.838
Number of iterations: 21
Estimated contemporaneous impact matrix:
prod e U rw
prod 0.58402 0.07434 -0.152578 0.06900
e -0.12029 0.26144 -0.155096 0.08978
U 0.02526 -0.26720 0.005488 0.04982
rw 0.11170 0.00000 0.483771 0.48791
Estimated long run impact matrix:
prod e U rw
prod 0.7910 0.0000 0.0000 0
e 0.2024 0.5769 -0.4923 0
U -0.1592 -0.3409 0.1408 0
rw -0.1535 0.5961 -0.2495 0
Covariance matrix of reduced form residuals (*100):
prod e U rw
prod 37.4642 -2.096 -0.2512 2.509
e -2.0960 11.494 -6.9273 -4.467
U -0.2512 -6.927 7.4544 2.978
rw 2.5087 -4.467 2.9783 48.457
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.