# ARXff: VGLTSMs family functions: Order-p Autoregressive Model with... In VGAMextra: Additions and Extensions of the 'VGAM' Package

 ARXff R Documentation

## VGLTSMs family functions: Order–p Autoregressive Model with covariates

### Description

Maximum likelihood estimation of the order–p autoregressive model (AR(p)) with covariates. Estimates the drift, standard deviation (or variance) of the random noise (not necessarily constant), and coefficients of the conditional–mean model.

### Usage

      ARXff(order    = 1,
zero     = c(if (nodrift) NULL else "ARdrift", "ARcoeff"),
xLag     = 0,
type.EIM = c("exact", "approximate")[1],
var.arg  = TRUE,
nodrift  = FALSE,
noChecks = FALSE,
idrift   = NULL,
isd      = NULL,
ivar     = NULL,
iARcoeff = NULL)



### Arguments

 order The order (i.e., 'p') of the AR model, which is recycled if needed. See below for further details. By default, an autoregressive model of order-1 is fitted. zero Integer or character–strings vector. Name(s) or position(s) of the parameters/linear predictors to be modeled as intercept-only. Details at zero. xLag Same as ARIMAXff. type.EIM, var.arg, nodrift, noChecks Same as ARIMAXff. ldrift, lsd, lvar, lARcoeff Link functions applied to the drift, the standar deviation (or variance) of the noise, and the AR coefficients. Same as ARIMAXff. Further details on CommonVGAMffArguments. idrift, isd, ivar, iARcoeff Same as ARIMAXff.

### Details

This family function describes an autoregressive model of order-p with covariates (ARX(p)). It is a special case of the subclass VGLM–ARIMA (Miranda and Yee, 2018):

 Y_t | \Phi_{t - 1} = \mu_t + \theta_{1} Y_{t - 1} + \ldots + \theta_p Y_{t - p} + \varepsilon_t,

where \boldsymbol{x}_t a (possibly time–varying) covariate vector and \mu_t = \mu^{\star} + \boldsymbol{\beta}^T \boldsymbol{x}_t is a (time–dependent) scaled–mean, known as drift.

At this stage, conditional Gaussian white noise, \varepsilon_t| \Phi_{t - 1} is handled, in the form

\varepsilon_t | \Phi_{t - 1} \sim N(0, \sigma^2_{\varepsilon_t | \Phi_{t - 1}}).

The distributional assumptions on the observations are then

Y_t | \Phi_{t - 1} \sim N(\mu_{t | \Phi_{t - 1}}, \sigma^2_{\varepsilon_t | \Phi_{t - 1}}), 

involving the conditional mean equation for the ARX(p) model:

\mu_{t | \Phi_{t - 1}} = \mu_t + \boldsymbol{\beta}^T * \boldsymbol{x}_t \theta_{1} Y_{t - 1} + \ldots + \theta_p Y_{t - p}.

\Phi_{t} denotes the information of the joint process \left(Y_{t}, \boldsymbol{x}_{t + 1}^T \right), at time t.

The loglikelihood is computed by dARp, at each Fisher scoring iteration.

The linear predictor is

\boldsymbol{\eta} = \left( \mu_t, \log \sigma^{2}_{\varepsilon_{t | \Phi_{t - 1}}}, \theta_1, \ldots, \theta_p \right)^T.

Note, the covariates may also intervene in the conditional variance model \log \sigma^{2}_{\varepsilon_{t | \Phi_{t - 1}}}. Hence, this family function does not restrict the noise to be strictly white noise (in the sense of constant variance).

The unconditional mean,  E(Y_{t}) = \mu, satisfies

\mu \rightarrow \frac{\mu^{\star}}{1 - (\theta_1 + \ldots + \theta_p)} 

when the process is stationary, and no covariates are involved.

This family function currently handles multiple responses so that a matrix can be used as the response. Also, for further details on VGLM/VGAM–link functions refer to Links.

Further choices for the random noise, besides Gaussian, will be implemented over time.

### Value

An object of class "vglmff" (see vglmff-class). The object is used by VGLM/VGAM modelling functions, such as vglm or vgam.

### Note

zero can be either an integer vector or a vector of character strings specifying either the position(s) or name(s) (partially or not) of the parameter(s) modeled as intercept-only. Numeric values can be set as usual (See CommonVGAMffArguments). Character strings can be entered as per parameter names in this family function, given by:

c("drift", "noiseVar" or "noiseSD", "ARcoeff").

Users can modify the zero argument according to their needs.

By default, \mu_t and the coefficients \theta_1, \ldots, \theta_p are intercept–only. That is, \log \sigma^{2}_{\varepsilon_{t | \Phi_{t - 1}}} is modelled in terms of any explanatories entered in the formula.

Users, however, can modify this according to their needs via zero. For example, set the covariates in the drift model, \mu_t. In addition, specific constraints for parameters are handled through the function cm.ARMA.

If var.arg = TRUE, this family function estimates \sigma_{\varepsilon_t | \Phi_{t - 1}}^2. Else, the \sigma_{\varepsilon_t | \Phi_{t - 1}} estimate is returned.

For this family function the order is recycled. That is, order will be replicated up to the number of responses given in the vglm call is matched.

### Warning

Values of the estimates may not correspond to stationary ARs, leading to low accuracy in the MLE estimates, e.g., values very close to 1.0. Stationarity is then examined, via checkTS.VGAMextra, if noChecks = FALSE (default) and no constraint matrices are set (See constraints for further details on this). If the estimated model very close to be non-stationary, then a warning will be outlined. Set noChecks = TRUE to completely ignore this.

NOTE: Full details on these 'checks' are shown within the summary() output.

### Author(s)

Victor Miranda and T. W. Yee

### References

Madsen, H. (2008) Time Series Analysis. Florida, USA: Chapman & Hall(Sections 5.3 and 5.5).

Porat, B., and Friedlander, B. (1986) Computation of the Exact Information Matrix of Gaussian Time Series with Stationary Random Components. IEEE Transactions on Acoustics, Speech and Signal Processing. ASSp-34(1), 118–130.

ARIMAXff, ARMAXff, MAXff, checkTS.VGAMextra, CommonVGAMffArguments, Links, vglm,

### Examples

set.seed(1)
nn     <- 150
tsdata <- data.frame(x2 =  runif(nn))             # A single covariate.
theta1 <- 0.45; theta2 <- 0.31; theta3 <- 0.10     # Coefficients
drift  <- c(1.3, -1.1)                             # Two responses.
sdAR   <- c(sqrt(4.5), sqrt(6.0))                  # Two responses.

# Generate AR sequences of order 2 and 3, under Gaussian noise.
# Note, the drift for 'TS2' depends on x2 !
tsdata  <-  data.frame(tsdata, TS1 = arima.sim(nn,
model = list(ar = c(theta1, theta1^2)),  rand.gen = rnorm,
mean = drift[1], sd = sdAR[1]),
TS2 = arima.sim(nn,
model = list(ar = c(theta1, theta2, theta3)), rand.gen = rnorm,
mean = drift[2] + tsdata$x2 , sd = sdAR[2])) # EXAMPLE 1. A simple AR(2), maximizing the exact log-likelihood # Note that parameter constraints are involved for TS1, but not # considered in this fit. "rhobitlink" is used as link for AR coeffs. fit.Ex1 <- vglm(TS1 ~ 1, ARXff(order = 2, type.EIM = "exact", #iARcoeff = c(0.3, 0.3, 0.3), # OPTIONAL INITIAL VALUES # idrift = 1, ivar = 1.5, isd = sqrt(1.5), lARcoeff = "rhobitlink"), data = tsdata, trace = TRUE, crit = "loglikelihood") Coef(fit.Ex1) summary(fit.Ex1) vcov(fit.Ex1, untransform = TRUE) # Conformable with this fit. AIC(fit.Ex1) #------------------------------------------------------------------------# # Fitting same model using arima(). #------------------------------------------------------------------------# (fitArima <- arima(tsdata$TS1, order = c(2, 0, 0)))
# Compare with 'fit.AR'. True are theta1 = 0.45; theta1^2 = 0.2025
Coef(fit.Ex1)[c(3, 4, 2)]    # Coefficients estimated in 'fit.AR'

# EXAMPLE 2. An AR(3) over TS2, with one covariate affecting the drift only.
# This analysis makes sense as the TS2's drift is a function ox 'x2',
# i.e., 'x2' affects the 'drift' parameter only. The noise variance
# (var.arg = TRUE) is estimated, as intercept-only. See the 'zero' argument.

#------------------------------------------------------------------------#
# This model CANNOT be fitted using arima()
#------------------------------------------------------------------------#
fit.Ex2 <- vglm(TS2 ~ x2,  ARXff(order = 3, zero = c("noiseVar", "ARcoeff"),
var.arg = TRUE),
## constraints = cm.ARMA(Model = ~ 1, lags.cm = 3, Resp = 1),
data = tsdata,  trace = TRUE, crit = "log")

# True are theta1 <- 0.45; theta2 <- 0.31; theta3 <- 0.10
coef(fit.Ex2, matrix = TRUE)
summary(fit.Ex2)
vcov(fit.Ex2)
BIC(fit.Ex2)
constraints(fit.Ex2)

# EXAMPLE 3. Fitting an ARX(3) on two responses TS1, TS2; intercept-only model with
#  constraints over the drifts. Here,
# a) No checks on invertibility performed given the use of cm.ARMA().
# b) Only the drifts are modeled in terms of 'x2'. Then,  'zero' is
# set correspondingly.
#------------------------------------------------------------------------#
# arima() does not handle this model.
#------------------------------------------------------------------------#
fit.Ex3 <- vglm(cbind(TS1, TS2) ~ x2, ARXff(order = c(3, 3),
zero = c("noiseVar", "ARcoeff"), var.arg = TRUE),
constraints = cm.ARMA(Model = ~ 1 + x2, lags.cm = c(3, 3), Resp = 2),
trace = TRUE, data = tsdata, crit = "log")

coef(fit.Ex3, matrix = TRUE)
summary(fit.Ex3)
vcov(fit.Ex3)
constraints(fit.Ex3)



VGAMextra documentation built on Nov. 2, 2023, 5:59 p.m.