## Large Bayesian VAR

**Moderators:** EViews Gareth, EViews Moderator, EViews Esther

### Re: Large Bayesian VAR

I keep getting the following error Dot,diagonal and solve require a vector argument" what does this refer to?

### Re: Large Bayesian VAR

Did you create random walk prior vector before the estimation?

Could you provide the data file and description of the model?

Could you provide the data file and description of the model?

### Re: Large Bayesian VAR

I have create random walk prior vector irw following LBVAR.pdf and fill it in the dialog box.

The data contains 39 variables from 2005m7 to 2017m8. Further, are there some rules in setting training sample and sample size?

Much appreciation for your reply!

The data contains 39 variables from 2005m7 to 2017m8. Further, are there some rules in setting training sample and sample size?

Much appreciation for your reply!

### Re: Large Bayesian VAR

What is your Eviews version? Can you run a example of the lbvar add-in? Sample size should not include the missing observations. Training sample should be at least 30-40.

### Re: Large Bayesian VAR

dakila wrote:What is your Eviews version? Can you run a example of the lbvar add-in? Sample size should not include the missing observations. Training sample should be at least 30-40.

I used Eviews8.0 to estimate the model. When I update Eviews to the latest version,everything goes well.

Much appreciation!

### Re: Large Bayesian VAR

Hi there

I am trying to estimate a LBVAR in using the add in but I have a couple of questions on the estimation procedure.

1. Is the model estimated following the hierarchical approach of Giannone et al (2012) in their paper prior selection for vector autoregressions?

2. Are the impulse responses formulated in the same manner as the Banbura et al paper which is referenced in the add in doc? I.e. can we specify slow moving versus fast moving variables for the computation of impulse responses?

3. In the add in how is the covariance prior specified i.e. the prior for the third block of dummies?

Hope that you can help with these questions.

Thanks,

Abigail

I am trying to estimate a LBVAR in using the add in but I have a couple of questions on the estimation procedure.

1. Is the model estimated following the hierarchical approach of Giannone et al (2012) in their paper prior selection for vector autoregressions?

2. Are the impulse responses formulated in the same manner as the Banbura et al paper which is referenced in the add in doc? I.e. can we specify slow moving versus fast moving variables for the computation of impulse responses?

3. In the add in how is the covariance prior specified i.e. the prior for the third block of dummies?

Hope that you can help with these questions.

Thanks,

Abigail

### Re: Large Bayesian VAR

1. Is the model estimated following the hierarchical approach of Giannone et al (2012) in their paper prior selection for vector autoregressions?

No.

2. Are the impulse responses formulated in the same manner as the Banbura et al paper which is referenced in the add in doc? I.e. can we specify slow moving versus fast moving variables for the computation of impulse responses?

Yes. the identification is recursive (cholesky). But you cannot specify slow moving versus fast moving variables. It is related with FAVAR model. If you use FAVAR add-in you can specify that variables.

3. In the add in how is the covariance prior specified i.e. the prior for the third block of dummies?

Yes.

### Re: Large Bayesian VAR

Thank you for your quick response dakila. How is the cholesky ordering specified? Is this taken from the ordering of the variables in the model specification?

### Re: Large Bayesian VAR

Thank you. Is it possible to view the final coefficient estimates and the impulse responses in an extractable format rather than a frozen graph object?

Thanks,

Abigail

Thanks,

Abigail

### Re: Large Bayesian VAR

The lbvar add-in is updated. It now includes the generalized IRF and option to save IRF.

### Re: Large Bayesian VAR

Thanks Dakila - could you explain to me the difference between the generalized and cholesky impulse response functions? Am i right in thinking that generalized allow you to estimate the impact of the shocks independent of the impact of the shock on other variables within the system, hence you do not need to specify an ordering?

Your help is much appreciated!

Your help is much appreciated!

### Re: Large Bayesian VAR

Unlike the traditional impulse response analysis, GIRF does not require orthogonalization of shocks and is invariant to the ordering of the variables in the VAR. In other words, the definition of GIRF is different from Cholesky IRF.

### Re: Large Bayesian VAR

Hi there,

Can I just confirm that the definition of the impulse responses is for a 1 standard deviation shock to the variable of interest when using the recursive/cholesky decomposition definition of the IRFs?

If this is the case in the output if I shock the fed funds rate by 1 s.d. the chart is showing 1 on the y axis does this mean a 1% increase in the fed funds rate is equivalent to the 1 s.d. shock? i.e. the fed funds rate would go from 5% to 6%?

And if my other variables are in logs how would I interpret the y axis in this case? Would it be that a 1 s.d. shock to the fed funds reduces US GDP by X% or would you take the exponential of the value of impact of the shock on GDP and that would be the level impact? in other words would I need to transform the impulse response function to get the impact on log variables or can these directly be interpreted as % changes?

Can I just confirm that the definition of the impulse responses is for a 1 standard deviation shock to the variable of interest when using the recursive/cholesky decomposition definition of the IRFs?

If this is the case in the output if I shock the fed funds rate by 1 s.d. the chart is showing 1 on the y axis does this mean a 1% increase in the fed funds rate is equivalent to the 1 s.d. shock? i.e. the fed funds rate would go from 5% to 6%?

And if my other variables are in logs how would I interpret the y axis in this case? Would it be that a 1 s.d. shock to the fed funds reduces US GDP by X% or would you take the exponential of the value of impact of the shock on GDP and that would be the level impact? in other words would I need to transform the impulse response function to get the impact on log variables or can these directly be interpreted as % changes?

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