Modelling algal blooms using vector autoregressive model with exogenous variables and long memory filter

2007 ◽  
Vol 200 (1-2) ◽  
pp. 130-138 ◽  
Author(s):  
Gilbert C.S. Lui ◽  
W.K. Li ◽  
Kenneth M.Y. Leung ◽  
Joseph H.W. Lee ◽  
A.W. Jayawardena
Keyword(s):  
Long Memory ◽  
Autoregressive Model ◽  
Algal Blooms ◽  
Vector Autoregressive Model ◽  
Exogenous Variables ◽  
Vector Autoregressive

scholarly journals A Spatial Panel Structural Vector Autoregressive Model with Interactive Effects and Its Simulation

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 883
Author(s):  
Yaqing Liu ◽  
Hongbing Ouyang ◽  
Xiaolu Wei
Keyword(s):  
Autoregressive Model ◽  
Common Factors ◽  
Interactive Effects ◽  
Vector Autoregressive Model ◽  
Finite Sample ◽  
Vector Autoregressive ◽  
Spatial Panel ◽  
The Common ◽  
Structural Vector Autoregressive Model ◽  
Structural Vector

The existing spatial panel structural vector auto-regressive model can effectively capture the time and spatial dynamic dependence of endogenous variables. However, the hypothesis that the common factors have the same effect for all spatial units is unreasonable. Therefore, incorporating time effects, spatial effects, and time-individual effects, this paper develops a more general spatial panel structural vector autoregressive model with interactive effects (ISpSVAR) that can reflect the different effects of common factors on different spatial units. Additionally, based on whether or not the common factors can be observed, this paper proposes procedures to estimate ISpSVAR separately and studies the finite sample properties of estimators by Monte Carlo simulation. The simulation results show the effectiveness of the proposed ISpSVAR model and its estimation procedures.

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