Granger Causality, Impulse Response, Variance Decomposition, and Forecasting in VAR using R

[@josephomwenga2951]

Thank you very very much sir... your videos have helped me ace my Time series term paper!! May God bless you and your work!!

[@MrGang12345]

You are the best!!! I was struggling to understand how to do VAR commands and this was the best help & explanation ever!! god bless you

[@noimnotyourex]

Thank you, I've been struggling with these concepts for a few weeks and I finally got it.

[@pedrocolangelo5844]

Mr Eloriaga, this is another outstanding video of yours. Thank you so much.

[@jummy7416]

This has been so helpful for my dissertation, thank you so much !

[@omolola3181]

Thank you for your good works. Your explanations are superb, easy to understand and follow.

[@risktakers5000]

Thank you Justin for your hard work on explanation

[@vilivuniivi5734]

Thanks very much for all your videos.

[@mufid.asshiddiq]

Thank you, this was very beneficial for me.

[@tianjoshua4079]

This is gold. Thanks a lot.

[@adokoka]

You are good, man!

[@florinaliu5489]

Fantastic Explanations Congradulation

[@smiley1136]

Thank you very much!

[@sakticharantasupalli9158]

Explained brilliantly!!!! could you please explain about "Nonlinear Granger causality test in R"

[@PeterJMPuyneers]

thank you very much, it helped me a lot in understanding

[@ENIdesigns]

Amazing video!

[@mahirtagizade2993]

Hi, Thank you for this video, I have a question. could you explain that, how do we know the Impulse response shocks are positive or negative?

[@JustinEloriaga]

You may refer to the sign of the y-axis as a basis as to if it was positive response to that variable or a negative response to that variable.

[@mahirtagizade2993]

@@JustinEloriaga Thank you for your response, Yes, it is true but I mean that how do we know the shocks are positive or negative? Someones claim that there is a mirror effect but when we use impulse response function in the economic analysis ( for instance interest rate impacts on investment or GDP) the results could be different because a high level of interest rate negatively affects the investment and vice versa. How do we know it?

[@michellehuang7583]

Awesome!!

[@rose_prtr]

Thank you so much

[@olanintola]

thank you

[@RustuYucel]

Excellent tut, but I wonder if we know one way relation btw two variables (ie A shock affects B but B shock not affects A) what should we do to measure impact in following periods? Looking fwd to hearing from you. Best regs

[@KerlX_X]

Thank you! great video! it is easy to understand. This video helps me to pass my asm, please keep doing it. I wonder if we are using negative value as the data, e.g. inflation rate, is it still work?

[@hardiksen2007]

shouldn't the confidence bands of the IRF be in the same quadrant?

[@ranjankumarnath4416]

Can I used vars modeling to predict the no.of causes of crimes

[@johannaw2031]

How could you get that FEVD plot just by typing plot? When I try I get nothing.

[@leahshitindi8365]

thank you so much for the great Videos. My question is: is that possible to estimate the PVAR model with an interaction term?

[@melaniegrouteau116]

Thank you, it was very clear :)

[@Qwerty-md1dd]

Could you please explain how i can do cumulative irf ? 🙏🏻🙏🏻🙏🏻

[@jimmymosiah4333]

Hi Justin, many thanks for your clear explanations. But I do not understand why you didn't test the data stationarity as precondition before bulding your VAR model. I'm currently working on my Master thesis, on US Monetary policy effects on commodity prices. According to several papers, data stationarity is a precondition VAR

[@gavin07722]

Thank you. Could you possibly also share the codes?

[@rosettekhalil2586]

Can plz someone tell me how to make the conditional granger causality on eviews what are the steps ?

[@risktakers5000]

Usualy after VAR follows VECM. Could you please make video lecture on VECM in R?

[@yoshitkhatadia9239]

Is it necessary to standardize the data before running the model?

[@saralvar]

Hi Justin, thanks for all your vids, you're probably rescuing my bachelor thesis hahah I just have a question, why did you not control for the structural break in the Unemployment Rate when modelling the VAR?? Isn't it necessary in order to not influence the model output with this break? I just did my model testing for cointegration through the Johansen test and had to control for a similar structural break when modelling the VAR as I couldn't get I(1) variables through tests (as they told me that the variables were I(2)), but only by economic reasoning of this break Thanks in advance!

[@markdekwaasteniet9428]

You've helped me a lot with the analysis in my thesis. Thank you very much! I still have one question. For the lags in the serial correlation test you use 12. I guess because you use monthly data. I've read on the internet that it depends on the interval of your data and that you should insert the maximum number of lags that you expect autocorrelation for. I am using daily and 30 min data. Would you say using pt.lags = 15 and 30 would be sufficient?

[@palashsrivastava7711]

Can you please guide me with how to go about forecasting when exog is not NULL? I want to forecast using VARS with an exogenous variable. Thanks!

[@JustinEloriaga]

Hi! You may want to specify the variable that is exogenous in that option instead of writing NULL.

[@palashsrivastava7711]

@@JustinEloriaga So just like GDP /Unemployment we introduce another continuous variable as the exogenous factor. Thanks a lot for your help!

[@masoumehsolgi7974]

hi, thank you for your video,i have 2 question regarding granger test. if we want to do test based on wald test ,how we could determine the no f lags which be part of lm test? if the variables are I(1),should we use VAR in difference ?and if yes ,should we do Granger causality with difference or level?

[@JustinEloriaga]

Hello, thank you for your comment. Regarding the first question on the implementation of a Wald's Test, to know the number of lags would be based upon the number of restrictions you are imposing. Normally, this is set to 1 or 2. On the second question, yes, it is advised to use a VAR in differences. The Granger causality should now be in the differenced form to be consistent in notation. However, if all variables in the system are I(1), you might want to consider a VECM model.

[@adilmasoodkhan4527]

@@JustinEloriaga Sir, when we do VECM based Granger causality then to get short run causality we need to perform Wlad test on every short run coefficients...so how can we do Wald test in R? plz tell.

[@raulq.3519]

Excellent video, Justin! An theoretical question, how can I interpret the impulse-response function as a moving average process?

[@JustinEloriaga]

Hello! Thank you for your comment. Essentially, an IRF describes how a variable evolves along a specified horizon or window of time post a shock in the current period. There is a way to transform a stationary VAR into a Vector Moving Average with a lag of infinity to better represent that the current value of a series is merely the aggregate sum of all past innovations. This is formally called Wold's Decomposition Representation. Essentially, if we take a partial derivative between the coefficient and an error, we get an impulse response. You may want to check out this article: www.ucl.ac.uk/~uctp041/Teaching_files/Tutorial_IRF.pdf . Sorry if this reply is a bit lengthy! Hope this helps.

[@raulq.3519]

@@JustinEloriaga Thanks!

[@raulq.3519]

Justin, is it possible to run the Granger test in R for more than two variables? I mean, is it possible to run this test on a matrix of n variables as a whole?

[@JustinEloriaga]

Hi! This was also a concern of mine. You would need to investigate the relationships one by one to my knowledge. You can specify which variable two variables at a time you would want to using some option in the command.

[@adilmasoodkhan4527]

@@JustinEloriaga Hi Sir, I think we can use more than two variables. and it is possible while writing cause option we may put variables two or more in vector form then look at the response on dependent / remaining variable that is treated as effect variable