scholarly journals Vector Autoregressive Model for Electricity Prices Subject to Long Memory and Regime Switching

2007 ◽  
Author(s):  
Niels Haldrup ◽  
Frank Nielsen ◽  
Morten Ørregaard Nielsen
Keyword(s):  
Long Memory ◽  
Regime Switching ◽  
Autoregressive Model ◽  
Vector Autoregressive Model ◽  
Electricity Prices ◽  
Vector Autoregressive

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 Markov Switching Intercept Vector Autoregressive Model (MSI(2)-VAR(2)) of Nigeria Inflation Rate and Crude Oil Price (Using Views 11)

2021 ◽  
Vol 4 (2) ◽  
pp. 88-100
Author(s):  
Wiri L. ◽  
Sibeate P.U. ◽  
Isaac D.E.
Keyword(s):  
Crude Oil ◽  
Regime Switching ◽  
Autoregressive Model ◽  
Inflation Rate ◽  
Markov Switching ◽  
Information Criterion ◽  
Information Criteria ◽  
Vector Autoregressive Model ◽  
Vector Autoregressive ◽  
Crude Oil Prices

To model inflation rate and crude oil prices, we used Markov Switching intercept heteroscedasticity Vector Autoregressive models. The data for this analysis was gathered from the Central Bank of Nigeria Statistical Bulletin monthly. The upward and downward movement in the series revealed by the time plot suggests that the series exhibit a regime-switching pattern: the period of expansion and contraction. The variable was stationary at first differences, the Augmented Dickey-Fuller test was used to screen for stationarity. The information criteria were used to test the number of regime and regime two were selected. Eight models were estimated for the MSI-VAR model. The best model was chosen based on the criterion of least information criterion, Markov-switching intercept heteroscedasticity – Vector Autoregressive model (MSIH(2)-VAR(2)) with AIC (8.596641) and SC (8.973119). The model was used to predict the series' values over a one-year cycle (12 months).

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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