summary.glarma

print.summary.glarma

glarmaModelEstimates

An object of class <code>"glarma"</code>, obtained from a call
   to <code><a rd-options="" href="/link/glarma?package=glarma&version=1.5-0" data-mini-rdoc="glarma::glarma">glarma</a></code>.

object

An object of class <code>"summary.glarma"</code>, obtained from a
   call to <code>summary.glarma</code>.

Numeric; minimum number of significant digits to be used
 for most numbers.

digits

Logical; if <code>TRUE</code>, the likelihood-ratio test and
 the Wald test are shown in the summary. The default is <code>TRUE</code>.

tests

Further arguments passed to or from other methods.


  <code>summary</code> method for class <code>glarma</code> and functions to
  generate the estimates for this <code>summary</code> method.


print

Functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables.  These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.

William TM Dunsmuir

glarma

Generalized Linear Autoregressive Moving Average Models

summary.glarma function

An object of class <code>"glarma"</code>, obtained from a call
   to <code><a rd-options='' href='glarma'>glarma</a></code>.

summary.glarma: Summarize GLARMA Fit

Description

Usage

Arguments

Value

See Also

Examples