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 nonlinear nonGaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressivemoving 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, scoretype 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.
Package details 


Author  William T.M. Dunsmuir <w.dunsmuir@unsw.edu.au>, Cenanning Li <cli113@aucklanduni.ac.nz>, and David J. Scott <d.scott@auckland.ac.nz> 
Date of publication  20170125 09:02:55 
Maintainer  "William T.M. Dunsmuir" <w.dunsmuir@unsw.edu.au> 
License  GPL (>= 2) 
Version  1.50 
Package repository  View on CRAN 
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