A Bivariate First-Order Autoregressive Time Series Model in Exponential Variables (BEAR(1))

Lee S. Dewald, Peter A. W. Lewis and Ed McKenzie
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Lee S. Dewald: United States Military Academy, West Point, New York 10996-1786
Peter A. W. Lewis: Department of Operations Research, Naval Postgraduate School, Monterey, California 93942-5000
Ed McKenzie: Department of Mathematics, University of Strathclyde, Glasgow, Scotland

Management Science, 1989, vol. 35, issue 10, 1236-1246

Abstract: A simple time series model for bivariate exponential variables having first-order autoregressive structure is presented, the BEAR(1) model. 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.

Keywords: time series; bivariate exponential distribution; autoregressive models; NEAR(2); ARMA(2; 1) models; Gaussian AR(1) bivariate time series model; BEAR(1) model (search for similar items in EconPapers)
Date: 1989
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Citations: View citations in EconPapers (3)

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