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

Description Usage Arguments Value Author(s) See Also Examples

Description

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

Usage

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## 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

See Also

VAR, SVAR, SVEC

Examples

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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) 

Example output

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

vars documentation built on May 1, 2019, 8:23 p.m.