Gaussian and Non-Gaussian Linear Time Series and Random Fields

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Noncausal Autoregressions for Economic Time Series (2010, unknown): “…literature on noncausal autoregressions, it has been common to specify the model as a (B) yt = εt, (8) where a (B) = 1 − a1B − · · · − apBp with ap 6= 0 and εt is an i.i.d. sequence with zero mean and finite variance (see, e.g., Breidt et al. (1991), Rosenblatt (2000) and the references therein).”
Filtering, Prediction and Simulation Methods for Noncausal Processes (2016, background): “While the empirical literature on noncausal and mixed processes is quite recent, the theoretical results have been long established since the 1970s and the 1980s (see, e.g. Davis and Resnick, 1986; or Rosenblatt, 2000 for a general presentation of such processes).”
Basic properties of strong mixing conditions. A survey and some open questions (2005, background): “For mixing properties of linear processes, see [71] and [143].”
Least absolute deviation estimation for all-pass time series models (2001, methodology): “The resulting estimates could be used as preliminary estimates in a more re ned estimation procedure as in Breidt, Davis, Lii, and Rosenblatt (1991). This two-step procedure avoids the need to study all possible 2 con gurations of roots inside and outside the unit circle.”