Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies cross models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

# S3 method for garch
tidy(x, ...)

Arguments

x

A garch object returned by tseries::garch().

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

See also

tidy(), tseries::garch()

Other garch tidiers: glance.garch

Value

A tibble::tibble() with columns:

estimate

The estimated value of the regression term.

p.value

The two-sided p-value associated with the observed statistic.

statistic

The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

std.error

The standard error of the regression term.

term

The name of the regression term.

Examples

library(tseries) data(EuStockMarkets) dax <- diff(log(EuStockMarkets))[,"DAX"] dax.garch <- garch(dax)
#> #> ***** ESTIMATION WITH ANALYTICAL GRADIENT ***** #> #> #> I INITIAL X(I) D(I) #> #> 1 9.549651e-05 1.000e+00 #> 2 5.000000e-02 1.000e+00 #> 3 5.000000e-02 1.000e+00 #> #> IT NF F RELDF PRELDF RELDX STPPAR D*STEP NPRELDF #> 0 1 -7.584e+03 #> 1 8 -7.585e+03 1.45e-05 2.60e-05 1.4e-05 1.0e+11 1.4e-06 1.35e+06 #> 2 9 -7.585e+03 1.88e-07 1.97e-07 1.3e-05 2.0e+00 1.4e-06 1.50e+00 #> 3 18 -7.589e+03 6.22e-04 1.10e-03 3.5e-01 2.0e+00 5.5e-02 1.50e+00 #> 4 21 -7.601e+03 1.58e-03 1.81e-03 6.2e-01 1.9e+00 2.2e-01 3.07e-01 #> 5 23 -7.634e+03 4.22e-03 3.55e-03 4.3e-01 9.6e-01 4.4e-01 3.06e-02 #> 6 25 -7.646e+03 1.61e-03 1.85e-03 2.9e-02 2.0e+00 4.4e-02 5.43e-02 #> 7 27 -7.646e+03 3.82e-05 5.23e-04 1.3e-02 2.0e+00 2.0e-02 1.46e-02 #> 8 28 -7.648e+03 1.86e-04 1.46e-04 6.5e-03 2.0e+00 9.9e-03 1.54e-03 #> 9 29 -7.648e+03 3.12e-05 4.83e-05 6.4e-03 2.0e+00 9.9e-03 3.34e-03 #> 10 30 -7.648e+03 1.39e-05 6.31e-05 6.2e-03 1.9e+00 9.9e-03 1.86e-03 #> 11 31 -7.650e+03 2.70e-04 3.24e-04 6.0e-03 1.9e+00 9.9e-03 4.99e-03 #> 12 34 -7.656e+03 8.42e-04 8.57e-04 2.2e-02 1.7e-01 3.9e-02 2.22e-03 #> 13 36 -7.661e+03 6.12e-04 6.40e-04 1.9e-02 4.2e-01 3.9e-02 2.09e-03 #> 14 38 -7.665e+03 4.87e-04 8.63e-04 4.9e-02 4.1e-01 9.6e-02 9.69e-04 #> 15 48 -7.666e+03 1.02e-04 1.86e-04 1.9e-07 4.5e+00 3.5e-07 3.94e-04 #> 16 49 -7.666e+03 1.12e-07 1.01e-07 1.9e-07 2.0e+00 3.5e-07 6.22e-05 #> 17 57 -7.666e+03 1.60e-05 2.70e-05 2.0e-03 9.3e-01 3.7e-03 6.10e-05 #> 18 59 -7.666e+03 5.23e-06 7.01e-06 3.7e-03 3.9e-01 8.0e-03 7.77e-06 #> 19 60 -7.666e+03 4.08e-08 3.74e-08 1.4e-04 0.0e+00 3.1e-04 3.74e-08 #> 20 61 -7.666e+03 2.31e-09 8.57e-10 8.6e-06 0.0e+00 2.0e-05 8.57e-10 #> 21 62 -7.666e+03 5.35e-11 2.25e-13 7.6e-07 0.0e+00 1.6e-06 2.25e-13 #> 22 63 -7.666e+03 1.81e-12 7.06e-16 1.7e-08 0.0e+00 3.4e-08 7.06e-16 #> 23 64 -7.666e+03 6.98e-14 1.69e-17 1.0e-09 0.0e+00 2.4e-09 1.69e-17 #> 24 65 -7.666e+03 -1.16e-14 1.76e-20 1.9e-10 0.0e+00 4.0e-10 1.76e-20 #> #> ***** X- AND RELATIVE FUNCTION CONVERGENCE ***** #> #> FUNCTION -7.665775e+03 RELDX 1.874e-10 #> FUNC. EVALS 65 GRAD. EVALS 24 #> PRELDF 1.760e-20 NPRELDF 1.760e-20 #> #> I FINAL X(I) D(I) G(I) #> #> 1 4.639289e-06 1.000e+00 -2.337e-02 #> 2 6.832875e-02 1.000e+00 -8.294e-07 #> 3 8.890666e-01 1.000e+00 -2.230e-06 #>
dax.garch
#> #> Call: #> garch(x = dax) #> #> Coefficient(s): #> a0 a1 b1 #> 4.639e-06 6.833e-02 8.891e-01 #>
tidy(dax.garch)
#> # A tibble: 3 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 a0 0.00000464 0.000000756 6.14 8.42e-10 #> 2 a1 0.0683 0.0113 6.07 1.25e- 9 #> 3 b1 0.889 0.0165 53.8 0.
glance(dax.garch)
#> # A tibble: 1 x 7 #> statistic p.value parameter method logLik AIC BIC #> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> #> 1 0.136 0.713 1 Box-Ljung test 5958. -11911. NA