A Semiparametric Estimation for the First-Order Nonlinear Autoregressive Time Series Model with Independent and Dependent Errors

2018 ◽  
Vol 43 (3) ◽  
pp. 905-917 ◽  
Author(s):  
Rahman Farnoosh ◽  
Mahtab Hajebi ◽  
S. Yaser Samadi
Keyword(s):  
Time Series ◽  
Semiparametric Estimation ◽  
Time Series Model ◽  
Autoregressive Time Series ◽  
First Order ◽  
Nonlinear Autoregressive

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 Modeling a nonlinear process using the exponential autoregressive time series model

2018 ◽  
Vol 95 (3) ◽  
pp. 2079-2092 ◽  
Author(s):  
Huan Xu ◽  
Feng Ding ◽  
Erfu Yang
Keyword(s):  
Time Series ◽  
Nonlinear Process ◽  
Time Series Model ◽  
Autoregressive Time Series
Sign in / Sign up

Export Citation Format

Share Document