Question

Causality between two variables:

Describe how we test for causality and why this might be important in conducting empirical analysis.

Answer 1

Causality : Causality is influence by which one event, process or state(cause) contributes to the production of another event, process or state(an effect) where the cause is partly responsible for the, and the effect is partly dependent on the cause.

For testing causality we use F test and chi-square test. Steps of testing is following:

1: State the Null hypothesis and alternative hypothesis. For example: Y(t) does not Granger-cause x(t).

2: Choose the lags. This mostly depends on how much data you have available. One way to choose lags i and j is to run a model order test.It might be easier just to pick several values and run the Granger test several times to see if the results are the same for different lag levels.

3: Find the f value

4: Calculate the f-statistic using the following equation:

F: {(ESSr-ESSur)/q}/ESSur/(n-k)

5: Reject the null if the F statistic (step 4) is greater than the f-value(step3)

If you have large number of variables and lags, your F-test can lose power. An alternative would be to run a chi-square test, constructed with likelihood ratio or wald tests.

It is important in emperical analysis because :

Causal research, also called explanatory research, is the investigation of cause-and effect relationships. To determine causality

, it is important to observe variation in the variable assumed to cause the change in the others variable, and then the measure the change in others variables.

The causality play a very important role in econometric and economics.