## Does cointegration mean correlation?

Cointegration is the existence of long-run relationship between two or more variables. However, the correlation does not necessarily means “long-run”. Correlation is simply a measure of the degree of mutual association between two or more variables.

**What does it mean if two variables are cointegrated?**

Two sets of variables are cointegrated if a linear combination of those variables has a lower order of integration. For example, cointegration exists if a set of I(1) variables can be modeled with linear combinations that are I(0).

### What do we mean by cointegration?

Cointegration is a statistical method used to test the correlation between two or more non-stationary time series in the long-run or for a specified time period. The method helps in identifying long-run parameters or equilibrium for two or more sets of variables.

**Why is cointegration important?**

Cointegration explicitly allows for nonstationarity, thus providing a sounder basis for empirical inference. Cointegration also clarifies the problem of nonsense regressions, in which intrinsically unrelated nonstationary time series are highly correlated with each other.

#### Does cointegration mean causality?

The cointegration will tell us the relationship of long run and short among these two while causality indicates either X is causing Y or either Y is causing X or either both varianles are casuing each other. cointegration is just a way to find associational linear relationship among non-stationary time-series.

**What is cointegration in time series?**

Cointegration is a statistical property of a collection (X1, X2., Xk) of time series variables. Formally, if (X,Y,Z) are each integrated of order d, and there exist coefficients a,b,c such that aX + bY + cZ is integrated of order less than d, then X, Y, and Z are cointegrated.

## What are the implications of cointegration?

**Can there be causality without cointegration?**

In the absence of cointegration, you can proceed with Simple Granger causality (unrestricted VAR). The VAR equation should be specified on stationary data. Moreover, with very few exception, granger causality is a test of a variable that is grager caused by its own lags and lags of other variables.