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

1986 ◽  
Author(s):  
Lee S. Dewald ◽  
Peter A. Lewis ◽  
Ed McKenzie
Keyword(s):  
Time Series ◽  
Time Series Model ◽  
Autoregressive Time Series ◽  
First Order

A new autoregressive time series model in exponential variables (NEAR(1))

1981 ◽  
Vol 13 (4) ◽  
pp. 826-845 ◽  
Author(s):  
A. J. Lawrance ◽  
P. A. W. Lewis
Keyword(s):  
Time Series ◽  
Autoregressive Model ◽  
Marginal Distribution ◽  
Sample Path ◽  
Cross Coupling ◽  
Time Series Model ◽  
Autoregressive Time Series ◽  
First Order ◽  
Joint Distributions ◽  
Two Parameter

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 (ear(1)), runs of constantly scaled values are avoidable, and the two parameter structure allows some adjustment of directional effects in sample path behaviour. 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.

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

1989 ◽  
Vol 35 (10) ◽  
pp. 1236-1246 ◽  
Author(s):  
Lee S. Dewald ◽  
Peter A. W. Lewis ◽  
Ed McKenzie
Keyword(s):  
Time Series ◽  
Time Series Model ◽  
Autoregressive Time Series ◽  
First Order

A new autoregressive time series model in exponential variables (NEAR(1))

1981 ◽  
Vol 13 (04) ◽  
pp. 826-845 ◽  
Author(s):  
A. J. Lawrance ◽  
P. A. W. Lewis
Keyword(s):  
Time Series ◽  
Autoregressive Model ◽  
Marginal Distribution ◽  
Sample Path ◽  
Cross Coupling ◽  
Time Series Model ◽  
Autoregressive Time Series ◽  
First Order ◽  
Joint Distributions ◽  
Two Parameter

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 (ear(1)), runs of constantly scaled values are avoidable, and the two parameter structure allows some adjustment of directional effects in sample path behaviour. 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.

scholarly journals Justification of Wold’s Theorem and the Unbiasedness of a Stable Vector Autoregressive Time Series Model Forecasts

2017 ◽  
Vol 6 (2) ◽  
pp. 1
Author(s):  
Iberedem A. Iwok
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Autoregressive Process ◽  
Time Series Model ◽  
Average Process ◽  
Moving Average Process ◽  
Autoregressive Time Series ◽  
Vector Autoregressive ◽  
Vector Autoregressive Process ◽  
Vector Time Series

In this work, the multivariate analogue to the univariate Wold’s theorem for a purely non-deterministic stable vector time series process was presented and justified using the method of undetermined coefficients. By this method, a finite vector autoregressive process of order  [] was represented as an infinite vector moving average () process which was found to be the same as the Wold’s representation. Thus, obtaining the properties of a  process is equivalent to obtaining the properties of an infinite  process. The proof of the unbiasedness of forecasts followed immediately based on the fact that a stable VAR process can be represented as an infinite VEMA process.

scholarly journals A Flexible Univariate Autoregressive Time‐Series Model for Dispersed Count Data

2020 ◽  
Vol 41 (3) ◽  
pp. 436-453 ◽  
Author(s):  
Kimberly F. Sellers ◽  
Stephen J. Peng ◽  
Ali Arab
Keyword(s):  
Time Series ◽  
Count Data ◽  
Time Series Model ◽  
Autoregressive Time Series
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