Simple models for positive-valued and discrete-valued time series with ARMA correlation structure
Authors
Lewis, Peter A. W.
Subjects
Models
Point processes
Discrete-Valued Time Series
Positive-Valued Time Series
ARMA correlation
ARMA processes
Marginal distribution
EARMA-type processes
DARMA-type processes
Autoregressive processes
Moving average
processes
Point processes
Discrete-Valued Time Series
Positive-Valued Time Series
ARMA correlation
ARMA processes
Marginal distribution
EARMA-type processes
DARMA-type processes
Autoregressive processes
Moving average
processes
Advisors
Date of Issue
1978-11
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Three models for positive-valued and discrete-valued stationary time series are discussed. All have the property that for a range of specified marginal distributions the time series have the same correlation structure as the usual linear, autoregressive-moving average (ARMA) model. The models differ in the range of marginal distributions which can be accommodated and in the simplicity and flexibility of each model. Specifically the EARMA-type processes can be extended from the exponential distribution to a rather narrow range of continuous distributions; the DARMA-type processes can be defined usefully for any discrete marginal distribution and are simple and flexible. Finally the marginally controlled semiMarkov generated process can be defined for any continuous or discrete positive-valued distribution and is therefore very flexible. However, the model suffers from some complexity and parametric obscurity
Type
Technical Report
Description
Series/Report No
Department
Operations Research (OR)
Organization
Identifiers
NPS Report Number
NPS55-78-033
Sponsors
Funding
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.