[R] Using the vars package to find time series corelations or impact

[inhoue]

Hi you all,

I have couple of questions regarding how to use the vars package (the vector
autoregression model) to find co-relation/ impact between multiple time
series. I am not majoring in economic, I just want to use vars to check how
those time series I had impacting each other. I also hope this post can give
those non-economic majors a step by step guide on how to start using the
vars package. I have googling all over the web and try to get a "VAR 101/
tutorial/ how to apply var step by step guide" but I couldn't find any.

I am wondering what should be the steps to approach what I need to achieve.
I have read ~300 posts here  about vars trying to see if similar questions
have already been asked, but I didn't see any.

The followings are my main questions and I have put them into steps on how I
should use vars to find the co-relation between my time series data:

Step 1. Check if the input data are stationary:
MY QUESTION: As far as I understand, before I even use the vars package, I
need to test and see if all my time series data are stationary, is it
correct? 
If it is not stationary, I cannot use vars? or I need to take log for all my
time series data? Is the vars package provide any testing command for that?
If so, will that command return T/F to the test? Or I need to interpret some
output numbers?

Step 2. Pick the lag value:
I also need to pick a lag value and the VARSselect from the vars package can
help on this, I am sure about this step. 

Step 3. Use the VAR() command:
I have read some book chapters on vars and I have read part of the paper
from Dr. Bernhard Pfaff on how to use the vars package. In the paper, he
talks about how to use different commends of the vars. I am especially
interested in running the following commands and have questions on them:

> library(vars)      #<---- use the vars package
> data(Canada)    #<---- use the Canada data

> var.2c <- VAR(Canada, p=2, type="const") #<---- execute the var command
> and store the result in var.2c

> summary(var.2c)    #<---- I do NOT really understand how to interpret the
> output of the summary command. There are 4 columns for the output:
> Estimate, Std. Error, t value, and Pr(> l t l). 
MY QUESTION: How are these value helps to find the co-relation/ impact of
each time series to each other???

> plot(var.2c)      #<---- 4 graphs are shown for each time series: Diagram
> of fit for "one time series", Residuals, ACF Residuals, PACF Residuals.
MY QUESTION: I am not sure how to read those graphs and how will they help
to find the time series data impact to each other. The graphs are shown in
page 6 of this document: VARS_how_to_use.pdf (To find it: put this pdf file
name in google.)

MY QUESTION: Is there any other commands I need to run to find the time
series data impact to each other? Above I listed 3 steps, is there anymore
step I need?

If you all have any idea on ANY of the above questions, please feel free to
jump in! I am really appreciate on any help from you all.

Thanks,
Vince.
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