Error correction model
Encyclopedia
An error correction model is a dynamical system
with the characteristics that the deviation of the current state from its long-run relationship will be fed into its short-run dynamics.
An error correction model is not a model that corrects the error in another model.
vector, and then this relationship can be utilized to develop a refined dynamic model which can have a focus on long-run or transitory aspect such as the two VECM of a usual VAR in Johansen test
.
model.
Dynamical system
A dynamical system is a concept in mathematics where a fixed rule describes the time dependence of a point in a geometrical space. Examples include the mathematical models that describe the swinging of a clock pendulum, the flow of water in a pipe, and the number of fish each springtime in a...
with the characteristics that the deviation of the current state from its long-run relationship will be fed into its short-run dynamics.
An error correction model is not a model that corrects the error in another model.
Long-run relationship
A rough long-run relationship can be determined by the cointegrationCointegration
Cointegration is a statistical property of time series variables. Two or more time series are cointegrated if they share a common stochastic drift.-Introduction:...
vector, and then this relationship can be utilized to develop a refined dynamic model which can have a focus on long-run or transitory aspect such as the two VECM of a usual VAR in Johansen test
Johansen test
In statistics, the Johansen test, named after Søren Johansen, is a procedure for testing cointegration of several I time series. This test permits more than one cointegrating relationship so is more generally applicable than the Engle–Granger test which is based on the Dickey–Fuller test for...
.
VECM
A vector error correction model (VECM) adds error correction features to a multi-factor model such as a vector autoregressionVector autoregression
Vector autoregression is a statistical model used to capture the linear interdependencies among multiple time series. VAR models generalize the univariate autoregression models. All the variables in a VAR are treated symmetrically; each variable has an equation explaining its evolution based on...
model.