Many Methods for

Many “accessor” methods for fracdiff objects, notably summary, coef, vcov, and logLik; further print() methods were needed.

Usage

# S3 method for class 'fracdiff'
coef(object, ...)
# S3 method for class 'fracdiff'
logLik(object, ...)
# S3 method for class 'fracdiff'
print(x, digits = getOption("digits"), ...)
# S3 method for class 'fracdiff'
summary(object, symbolic.cor = FALSE, ...)
# S3 method for class 'summary.fracdiff'
print(x, digits = max(3, getOption("digits") - 3),
        correlation = FALSE, symbolic.cor = x$symbolic.cor,
        signif.stars = getOption("show.signif.stars"), ...)
# S3 method for class 'fracdiff'
fitted(object, ...)
# S3 method for class 'fracdiff'
residuals(object, ...)
# S3 method for class 'fracdiff'
vcov(object, ...)

Arguments

x, object: object of class fracdiff.
digits: the number of significant digits to use when printing.
...: further arguments passed from and to methods.
correlation: logical; if TRUE, the correlation matrix of the estimated parameters is returned and printed.
symbolic.cor: logical. If TRUE, print the correlations in a symbolic form (see symnum) rather than as numbers.
signif.stars: logical. If TRUE, “significance stars” are printed for each coefficient.

Author

Martin Maechler; Rob Hyndman contributed the residuals() and fitted() methods.

Examples

set.seed(7)
ts4 <- fracdiff.sim(10000, ar = c(0.6, -.05, -0.2), ma = -0.4, d = 0.2)
modFD <- fracdiff( ts4$series, nar = length(ts4$ar), nma = length(ts4$ma))
#> Warning: unable to compute correlation matrix; maybe change 'h'
## -> warning (singular Hessian) %% FIXME ???
coef(modFD) # the estimated parameters
#>           d         ar1         ar2         ar3          ma 
#>  0.18574785  0.60448721 -0.02589436 -0.21820311 -0.41066512 
vcov(modFD)
#>                 d           ar1           ar2           ar3           ma1
#> d    3.948926e-06 -5.861285e-07 -1.543319e-07 -1.320697e-06  2.842259e-06
#> ar1 -5.861285e-07 -2.612427e-06 -2.661677e-05  2.804338e-05 -2.883319e-05
#> ar2 -1.543319e-07 -2.661677e-05  1.222974e-04  8.663898e-06  6.348246e-05
#> ar3 -1.320697e-06  2.804338e-05  8.663898e-06 -1.066487e-05  4.522663e-06
#> ma1  2.842259e-06 -2.883319e-05  6.348246e-05  4.522663e-06  1.007082e-04
smFD <- summary(modFD)
smFD
#> 
#> Call:
#>   fracdiff(x = ts4$series, nar = length(ts4$ar), nma = length(ts4$ma)) 
#> 
#> *** Warning during (fdcov) fit: unable to compute correlation matrix; maybe change 'h'
#> 
#> Coefficients:
#>     Estimate
#> d      0.186
#> ar1    0.604
#> ar2   -0.026
#> ar3   -0.218
#> ma    -0.411
#> sigma[eps] = 1.006018 
#> [d.tol = 0.0001221, M = 100, h = 0.0001501]
#> Log likelihood: -1.425e+04 ==> AIC = 28510.24 [6 deg.freedom]
coef(smFD) # gives the whole table
#>        Estimate
#> d    0.18574785
#> ar1  0.60448721
#> ar2 -0.02589436
#> ar3 -0.21820311
#> ma  -0.41066512
AIC(modFD) # AIC works because of the logLik() method
#> [1] 28510.24
stopifnot(exprs = {

})

Many Methods for "fracdiff" Objects

Usage

Arguments

Author

See also

Examples