Convert VEC model to VAR model

Econometrics Toolbox™ VAR model functions such as `simulate`

, `forecast`

, and `armairf`

are appropriate for vector autoregression
(VAR) models. To simulate, forecast, or generate impulse responses
from a vector error-correction
(VEC) model using `simulate`

, `forecast`

,
or `armairf`

, respectively, convert the VEC model
to its equivalent VAR model representation.

returns
the coefficient matrices (`VAR`

= vec2var(`VEC`

,`C`

)`VAR`

) of the vector autoregressive
model equivalent to the vector error-correction model with coefficient
matrices (`VEC`

). If the number of lags in the input
vector error-correction model is *q*, then the number
of lags in the output vector error-correction model is *p* = *q* +
1.

To accommodate structural VEC models, specify the input argument

`VEC`

as a`LagOp`

lag operator polynomial.To access the cell vector of the lag operator polynomial coefficients of the output argument

`VAR`

, enter`toCellArray(VAR)`

.To convert the model coefficients of the output argument from lag operator notation to the model coefficients in difference-equation notation, enter

VARDEN = toCellArray(reflect(VAR));

`VARDEN`

is a cell vector containing*q*+ 1 coefficients corresponding to the response terms in`VAR.Lags`

in difference-equation notation. The first element is the coefficient of*y*, the second element is the coefficient of_{t}*y*_{t–1}, and so on.The constant offset of the converted VAR model is the same as the constant offset of the VEC model.

`vec2var`

does not impose stability requirements on the coefficients. To check for stability, use`isStable`

.`isStable`

requires a`LagOp`

lag operator polynomial as input. For example, to check whether`VAR`

, the cell array of-by`n`

numeric matrices, composes a stable time series, enter`n`

varLagOp = LagOp([eye(

*n*) var]); isStable(varLagOp)A

`0`

indicates that the polynomial is not stable. If`VAR`

is a`LagOp`

lag operator polynomial, then pass it to`isStable`

.

[1] Hamilton, J. D. *Time Series Analysis*.
Princeton, NJ: Princeton University Press, 1994.

[2] Lutkepohl, H. "New Introduction to Multiple Time Series Analysis." Springer-Verlag, 2007.