A Bivariate First-Order Autoregressive Time Series Model in Exponential Variables (BEAR(1))
提出一个针对双变量指数变量的简单时间序列模型BEAR(1)，具有一阶自回归结构，其相关性与高斯AR(1)双变量模型类似，能处理正相关和交叉相关，对时间序列研究者有用。
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.