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The direction of causality between these variables is determined via vector error correction model (VECM).
Spatial cross correlated information is incorporated with a piecewise linear vector error correction model.
Vector error calculation between the virtually simulated intermaxillary position and the intraoperative intermediate intermaxillary relationship revealed high accuracy.
We propose Time Space Threshold Vector Error Correction (TS-TVEC) model for short term (hourly) traffic state prediction.
In particular, the offered derivations provide for the residual field vector (error) in its incremental and total forms along with an explicit expression for the Jacobian matrix.
The controller is determined by parabolic switching functions of the scalar error quaternion and the transformed vector error quaternions and their rates.
Using a cointegrating vector error correction framework, this paper tests the hypothesized political business cycle for its effect on steel prices.
In particular, a cointegration relation is determined among the considered variables and the vector error correction methodology is used for the identification.
The methodology includes the use of Phillips and Perron (PP) tests, a cointegration model with vector error correction modeling (VECM) and vector autoregression (VAR) with Granger causality.
This paper proposes an automated approach to cointegrated system modeling that uses adaptive shrinkage techniques to estimate vector error correction models with unknown cointegrating rank structure and unknown transient lag dynamic order.
The examination of the long-run equilibrium of the mortality rate relies on co-integration analysis, and a vector error correction model (VECM) is proposed for mortality forecasts.
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