Contents
What do you need to know about cointegrated var?
The cointegrated VAR approach combines differences of variables with cointegration among them and by doing so allows the user to study both long-run and short-run effects in the same model.
Do you need an error correction model for cointegrated variables?
Models for cointegrated variables should have an error-correction representation; otherwise the model would be misspecified (cointegration goes hand-in-hand with the error correction representation).
How to test the nonstationary series for cointegration?
Test each pair of the nonstationary series ( x 1 and x 2; x 1 and x 3; x 2 and x 3) for cointegration using the Johansen or the Engle-Granger test. Then test all three series ( x 1, x 2, x 3) for cointegration using the Johansen test. In general, you want the following:
Can a stationary dependent variable be a nonstationary variable?
Models for stationary dependent variables should not have nonstationary explanatory variables (except perhaps for stationary combinations of cointegrated nonstationary variables); otherwise the linear combination of the regressors would diverge from the regressand.
How are cointegrated models used in statistical inference?
Problems in statistical inference immediately confronted researchers using cointegrated models. At the heart of cointegrated models are “integrated” variables, and statistics constructed from integrated variables often behave in nonstandard ways.
In 2006 Katarina Juselius published her book The Cointegrated VAR Model: Methodology and Applications, which discussed how to use the cointegrated VAR (CVAR) model as a statistically well-founded empirical methodology for inference in economic models.
What are the root problems of cointegrated models?
At the heart of cointegrated models are “integrated” variables, and statistics constructed from integrated variables often behave in nonstandard ways. “Unit root” problems are present and a large research effort has attempted to understand and deal with these problems.