A bivariate first order autoregressive time series model in exponential variables (BEAR (1))
By: Lee S. Dewald, Peter A.W. Lewis, Ed McKenzie
Contributors: Lewis, Peter A. W. 1932-; McKenzie, Edward; Naval Postgraduate School (U.S.). Dept. of Operations Research
Publisher: Naval Postgraduate School; Available from National Technical Information Service
Published: 1986
Place of publication: Monterey, Calif; Springfield, Va
Language: eng
Number of pages: 32
Subjects: BIVARIATE ANALYSIS; TIME SERIES ANALYSIS; MATHEMATICAL  MODELS
Other titles: NPS-55-86-019.
Description: A simple time series model for bivariate exponential variables having first-order      auto-regressive structure is presented. The linear random coefficient difference equation      model is an adaptation of the New Exponential Autoregressive model (NEAR (2)). The      process is Markovian in the bivariate sense and has correlation structure analogous to      that of the Gaussian AR(1) bivariate time series model. The model exhibits a full range      of positive correlations and cross-correlations. With some modification in either the      innovation or the random coefficients, the model admits some negative values for the      cross- correlations. The marginal processes are shown to have correlation structure of      ARMA (2,1) models.
Notes: Title from cover.

"NPS-55-86-019."

"October 1986."

AD A177 055.

Includes bibliographical references (p. 30-31).