A New Autoregressive Time Series Model in Exponential Variables (NEAR(1)).
NAVAL POSTGRADUATE SCHOOL MONTEREY CA
Pagination or Media Count:
A new time series model for exponential variables having first order autoregressive structure is presented. Unlike the recently studied standard autoregressive model in exponential variables EAR1, runs of constantly scaled values are avoidable, and the two parameter structure allows some adjustment of time nonreversibility effects in sample path behavior. The model is further developed by the use of cross-coupling and antithetic ideas to allow negative dependency. Joint distributions and autocorrelations are investigated. A transformed version of the model has a uniform marginal distribution and its correlation and regression structures are also obtained. Estimation aspects of the models are briefly considered. Author
- Statistics and Probability