We show how several models with moving average errors can be easily rewritten as models with autoregressive errors, thereby simplifying inference.

We show that the use of prior information derived from former empirical findings and/or subject matter theory regarding the lag structure of the observable variables together with an AR process for the error terms can produce univariate and single equation models that are intuitively appealing,...

We show how several models with moving average errors can be easily rewritten as models with autoregressive errors, thereby simplifying inference.

We show that the use of prior information derived from former empirical findings and/or subject matter theory regarding the lag structure of the observable variables together with an AR process for the error terms can produce univariate and single equation models that are intuitively appealing,...