In this paper we consider the estimation of models with autoregressive, moving average or error spatial dependence structure. Our definition of higher order models is an extension of the definition in Huang (1984) and allows to model for instance border effects or different kinds of connectivity within one model. We introduce a distance measure within the parameterspace and use this measure to define a penalty function that is needed to estimate some of the models. Simulation evidence is given on the performance of likelihood based estimation methods.
Download Info
To our knowledge, this item is not available for
download. To find whether it is available, there are three
options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page
whether it is in fact available.
3. Perform a search for a similarly titled item that would be
available.
For technical questions regarding this item, or to correct its listing, contact: (Walther Schoonenberg).
Related research
Keywords:
Other versions of this item:
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)