Skip to main content
Cross Validated

Return to Question

added 2 characters in body
Source Link
Sympa
Sympa
  • 7.9k
  • 3
  • 37
  • 60

We all understand that the Granger Causality test entails constructing two models. The first one is simply an autoregressive model with $y_{t}$$y_{t-1}$ being the single independent variable. The second one is adding an $x_{t-1}$ to the autoregressive model. Next, you check whether the residuals of those two models are different using an $F$-test. If they are, you can say that $x$ Granger-causes $y$.

However, within the "vars" package in R, when you use the causality function it works differently. That's because you can only specify what is $x$. The package's author calls it a "cause" variable. But you can't specify what is $y$, or the response variable. So when the R output comes out, you get that $x$ Granger-causes several different variables simultaneously with one single given $p$-value.

Within the framework of Granger Causality as depicted above, this does not seem to make much sense. I can understand variable $A$ Granger-causes variable $B$. But I don't understand how variable $A$ Granger-causes variables $B$, $C$, $D$ with a single $p$-value estimate just as if you had done $A$ Granger-causes $B$ alone.

We all understand that the Granger Causality test entails constructing two models. The first one is simply an autoregressive model with $y_{t}$ being the single independent variable. The second one is adding an $x_{t-1}$ to the autoregressive model. Next, you check whether the residuals of those two models are different using an $F$-test. If they are, you can say that $x$ Granger-causes $y$.

However, within the "vars" package in R, when you use the causality function it works differently. That's because you can only specify what is $x$. The package's author calls it a "cause" variable. But you can't specify what is $y$, or the response variable. So when the R output comes out, you get that $x$ Granger-causes several different variables simultaneously with one single given $p$-value.

Within the framework of Granger Causality as depicted above, this does not seem to make much sense. I can understand variable $A$ Granger-causes variable $B$. But I don't understand how variable $A$ Granger-causes variables $B$, $C$, $D$ with a single $p$-value estimate just as if you had done $A$ Granger-causes $B$ alone.

We all understand that the Granger Causality test entails constructing two models. The first one is simply an autoregressive model with $y_{t-1}$ being the single independent variable. The second one is adding an $x_{t-1}$ to the autoregressive model. Next, you check whether the residuals of those two models are different using an $F$-test. If they are, you can say that $x$ Granger-causes $y$.

However, within the "vars" package in R, when you use the causality function it works differently. That's because you can only specify what is $x$. The package's author calls it a "cause" variable. But you can't specify what is $y$, or the response variable. So when the R output comes out, you get that $x$ Granger-causes several different variables simultaneously with one single given $p$-value.

Within the framework of Granger Causality as depicted above, this does not seem to make much sense. I can understand variable $A$ Granger-causes variable $B$. But I don't understand how variable $A$ Granger-causes variables $B$, $C$, $D$ with a single $p$-value estimate just as if you had done $A$ Granger-causes $B$ alone.

added 27 characters in body; edited title
Source Link
Richard Hardy
Richard Hardy
  • 69.5k
  • 13
  • 126
  • 278

Does the Granger Causality test in the vars"vars" package make sense?

We all understand that the Granger Causality test entails constructing two models. TheThe first one is simply an autoregressive model with Y t-1$y_{t}$ being the single independent variable. TheThe second one is adding an X t-1$x_{t-1}$ to the autoregressive model. NextNext, you check whether the residuals of those two models are different using an F test$F$-test. And, ifIf they are, you can say that X$x$ Granger-cause Ycauses $y$. However

However, within the vars"vars" package in R, when you use the causality()causality function it works differently. That's because you can only specify what is X$x$. The packageThe package's author calls it a 'cause'"cause" variable. But,But you can't specify what is Y$y$, or the response variable. So,So when the R output comes out, you get that X$x$ Granger-causecauses several different variables simultaneously with a one single given p$p$-value level. Within

Within the framework of Granger Causality as depicted above, this does not seem to make much sense. II can understand variable A$A$ Granger-causecauses variable B$B$. But,But I don't understand how variable A$A$ Granger causes-causes variables B$B$, C$C$, D$D$ with a single p value$p$-value estimate just as if you had done A$A$ Granger-causes B$B$ alone.

Does the Granger Causality test in the vars package make sense?

We all understand that the Granger Causality test entails constructing two models. The first one is simply an autoregressive model with Y t-1 being the single independent variable. The second one is adding an X t-1 to the autoregressive model. Next, you check whether the residuals of those two models are different using an F test. And, if they are you can say that X Granger-cause Y. However, within the vars package in R, when you use the causality() function it works differently. That's because you can only specify what is X. The package author calls it a 'cause' variable. But, you can't specify what is Y, or the response variable. So, when the R output comes out, you get that X Granger-cause several different variables simultaneously with a one single given p-value level. Within the framework of Granger Causality as depicted above, this does not seem to make much sense. I can understand variable A Granger-cause variable B. But, I don't understand how variable A Granger causes variables B, C, D with a single p value estimate just as if you had done A Granger-causes B alone.

Does the Granger Causality test in the "vars" package make sense?

We all understand that the Granger Causality test entails constructing two models. The first one is simply an autoregressive model with $y_{t}$ being the single independent variable. The second one is adding an $x_{t-1}$ to the autoregressive model. Next, you check whether the residuals of those two models are different using an $F$-test. If they are, you can say that $x$ Granger-causes $y$.

However, within the "vars" package in R, when you use the causality function it works differently. That's because you can only specify what is $x$. The package's author calls it a "cause" variable. But you can't specify what is $y$, or the response variable. So when the R output comes out, you get that $x$ Granger-causes several different variables simultaneously with one single given $p$-value.

Within the framework of Granger Causality as depicted above, this does not seem to make much sense. I can understand variable $A$ Granger-causes variable $B$. But I don't understand how variable $A$ Granger-causes variables $B$, $C$, $D$ with a single $p$-value estimate just as if you had done $A$ Granger-causes $B$ alone.

Source Link
Sympa
Sympa
  • 7.9k
  • 3
  • 37
  • 60

Does the Granger Causality test in the vars package make sense?

We all understand that the Granger Causality test entails constructing two models. The first one is simply an autoregressive model with Y t-1 being the single independent variable. The second one is adding an X t-1 to the autoregressive model. Next, you check whether the residuals of those two models are different using an F test. And, if they are you can say that X Granger-cause Y. However, within the vars package in R, when you use the causality() function it works differently. That's because you can only specify what is X. The package author calls it a 'cause' variable. But, you can't specify what is Y, or the response variable. So, when the R output comes out, you get that X Granger-cause several different variables simultaneously with a one single given p-value level. Within the framework of Granger Causality as depicted above, this does not seem to make much sense. I can understand variable A Granger-cause variable B. But, I don't understand how variable A Granger causes variables B, C, D with a single p value estimate just as if you had done A Granger-causes B alone.