summaryS4VGAMextra-methods | R Documentation |

S4 summary methods for models fitted with time series family functions from VGAMextra.

These function are all
`methods`

for objects of class
`vglm`

with signature
`vgltsmff-class`

.

Implementation of vector generalized time series (TS) family functions
(*vgltsmff*) in VGAMextra is entirely based on the structure
of family functions of class
`vglmff-class`

from VGAM. More precisely, *vgltsmff* family functions
can be created by calls of the form `new("vgltsmff",...)`

,
following the structure `vglmff-class`

.
See `vglmff-class`

for
additional details.

In this line, specific S4 dispatching methods are currently implemented at VGAM to show (or plot) essential statistical information about the model fitted.

For the generic `summary`

, specifically, S4 methods for
objects with signature `vgtsff`

are incorporated
in VGAMextra to display supplementary analyses commonly
required by TS practicioners. That is, additional information
to the default output shown by
`summaryvglm`

for family functions
at VGAM, as follows:

a) The standard errors, which are computed from the asymptotic distribution of the MLE estimates, unlike the asymptotic approach (z-value) from VGAM.

b) Checks on stationarity and/or invertibility for autoregressive (AR), moving average (MA), and autoregressive moving-average (ARMA) models via the polynomial roots.

c) The AIC, AICC and BIC criteria for model identification.

Notice that, for intercept-only models in the 'vglm' context,
the asypmtotic distribution of the estimates, either conditional
or unconditional, will coincide with the theoretical distributions
as long as `n`

increases. In particular, for the AR(`p`

)
process, the MLEs and the Yule-Walker estimates will concur
asymptotically.

Where covariates or parameter constraints are involved,
the standard errors for the estimates from time series
family functions at VGAMextra are calculated from the
predicted values allocated in the slot `@predictors`

,
when `summary(...)`

is called.
In this case, the *conditional* mean,
`\textrm{E}[\eta_j | \textbf{x}]`

**x** `]`

, is considered as the estimate, where:

`\eta_j = \sum_{k = 1}^{p} \beta_{(j)k} \times x_{k},`

for `j = 1, \ldots, M`

.

An object of class
`summary.vglm`

printed by specific methods defined
at VGAMextra for objects with signature
vgltsff-class.

As for the intercept, notice that this is called *drift-term*
at `ARXff`

and
`ARMAXff`

,
whilst it is refered as *intercept*
in `MAXff`

.
This parameter is also estimated by TS family
functions in VGAMextra. In the MA model, particularly,
it is the mean of the process.

The drift-term,
denoted as `\mu^*`

,
is linearly linked to the mean,
`\mu`

,
of the AR and ARMA processes in turn, as follows:

` \mu \to \frac{\mu^{*} }{1 - \sum \theta_i}. `

Here, `\theta_i`

are the AR coefficients.
Hence, the standard error for the *drift-term* is
accordingly computed based on the asymptotic distribution
of the mean. More precisely, the relation

```
V(\mu^{*}) = (1 - \sum \theta_i)^{-2} \times
\frac{\sigma_{\varepsilon}^2 }{n},
```

is considered,
where `\sigma_{\varepsilon}^2`

is the variance
of the random errors.

Finally, the AIC, AICC and BIC criteria are computed of the well-known expressions

`AIC = (-2) \times Log-likelihood + 2 \times k`

`AICC = AIC + \frac{2~k~(k + 1)}{n - k - 1}`

and

`BIC = (-2) \times Log-likelihood + k~\times~ln(n)`

with `k`

denoting the number of parameters.

V. Miranda and T.W. Yee.

Woodward, H., Gray, H. and Elliot A. (2012)
*Applied Time Series Analysis*.
Taylor & Francis/CRC, Florida, USA.

`vgtsff-class`

,
`summaryvlgm`

,
`ARXff`

,
`MAXff`

,
`ARMAXff`

,
`vglm`

.

```
#------------------------------------------------------------------------#
# Fitting a simple Moving Average model to compare with arima().
#------------------------------------------------------------------------#
set.seed(0628)
nn <- 300
theta <- c(0.2, -0.37) # Autoregressive coefficients
phi <- c(0.25) # MA coefficients.
mu <- c(1.5, 0.85) # Mean (not drift) of the MA process.
x2 <- runif(nn)
tsd1 <- mu[1]/(1 - sum(theta)) +
arima.sim(n = nn,
model = list(order = c(2, 0, 0),
ar = theta),
sd = exp(1.5))
tsd2 <- mu[2]/(1 - sum(theta)) +
arima.sim(n = nn,
model = list(order = c(2, 0, 1),
ar = theta, ma = phi),
sd = exp(1 + 2 * x2))
tsdata <- data.frame(TS1 = tsd1, TS2 = tsd2, x2 = x2)
head(tsdata)
### An ARIMA(2, 0, 0) model, that is an AR(2) model ###
#fit1 <- vglm(TS1 ~ 1,
# ARIMAXff(order = c(2, 0, 0), var.arg = FALSE, type.EIM = "exact"),
# data = tsdata, crit = "log", trace = TRUE)
fit1 <- vglm(TS1 ~ 1,
ARXff(order = 2, var.arg = FALSE, type.EIM = "exact"),
data = tsdata, crit = "log", trace = TRUE)
m.coe <- Coef(fit1)
## Using arima to compare to summary(vgtsff)
summary(fit1)
arima(tsdata$TS1, order = c(2, 0, 0)) ## Similar SE's than VGAMextra.
m.coe[1] / (1 - sum(m.coe[-(1:2)])) # THIS IS SIMILAR TO THE INTERCEPT
# ESTIMATED BY arima(): 1.1898
### An ARIMA(2, 0, 1) models, that is an ARMA(2, 1) ###
### The errors standard deviation is a function of 'x2' ###
### NOTICE: ARIMA and ARMA use the "identitylink" for coefficients ###
#fit2 <- vglm(TS2 ~ x2,
# ARMAXff(order = c(2, 1), var.arg = FALSE, type.EIM = "exact",
# zero = NULL),
# # constraints = list('x2' = rbind(0, 1, 0, 0, 0)),
# data = tsdata, crit = "loglikelihood", trace = TRUE)
#m.coe <- coef(fit2)
#coef(fit2, matrix = TRUE)
## Compare summary(vglm) to arima().
#summary(fit2)
#arima(tsdata$TS2, order = c(2, 0, 1))
```

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