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

Abstract

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.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1986
Accession Number
ADA177055

Entities

People

  • Ed Mckenzie
  • Lee S. Dewald
  • Peter A. Lewis

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Autocorrelation
  • Classification
  • Coefficients
  • Cross Correlation
  • Data Science
  • Difference Equations
  • Equations
  • Information Science
  • Probability
  • Random Variables
  • Schools
  • Security
  • Statistical Analysis
  • Statistics
  • Surveys
  • United States
  • United States Military Academy

Fields of Study

  • Mathematics

Readers

  • Statistical inference.