|WikiProject Statistics||(Rated Start-class, Low-importance)|
|WikiProject Economics||(Rated Start-class, Low-importance)|
I just wanted to add the original paper here:
Granger, C.W.J., 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37 (3), 424– 438. —Preceding unsigned comment added by 220.127.116.11 (talk) 20:01, 7 March 2008 (UTC)
What can I read about this please? Any easy to understand explanations?
You can google "Granger Causality" and you will get different articles and Powerpoint presentations from either researchers or university professors. Some of those are easier to understand than others. It is easier to explain Granger Causality by walking someone through an example as I have by answering your second question just below.
Does this mean that eg. forecasts could be made of sectors in the stock market, whereas previously you could not tell if these lagged correlations were just coincidence or not?
You could use Granger Causality to test whether certain independent variables have an impact on stock market sectors. For instances, testing that Oil prices "Granger cause" energy companies stock prices you would do the following:
- You would first build a base model with just lagged stock prices (last month for instance) as the independent variable to predict current stock price.
- You build a test model by adding lagged oil prices as a second independent variable.
- You compare the squared residuals of the base model and the test model and you test whether they are different using the F test. You could just as well use the unpaired t test or its nonparametric counterpart the Mann-Whitney test. Using either of these tests, you will get a p value giving you the probability that the tested independent variable truly provides you additional explanatory information.
Sympa 19:15, 18 September 2006 (UTC)Sympa
mathematical section is flawed
Because it makes reference to an index "p" that does not appear in the formulae.