Search found 8 matches

by Andy_T
Wed Jul 02, 2014 9:20 am
Forum: VARs (Vector Autoregression Models)
Topic: Standard deviation to percentage changes interpretation
Replies: 25
Views: 49414

Re: Standard deviation to percentage changes interpretation

The reason for not sticking with percentages is to make the impulse responses directly interpretable as fiscal "multipliers", i.e. one dollar of G changes Y by x dollars. It is indeed very common in the literature to evaluate Y/G at the sample average. In any case, would you say that for a...
by Andy_T
Tue Jul 01, 2014 6:02 pm
Forum: VARs (Vector Autoregression Models)
Topic: Standard deviation to percentage changes interpretation
Replies: 25
Views: 49414

Re: Standard deviation to percentage changes interpretation

At the risk of beating the dead horse and apologizing for bringing variations of this up again.... When estimating a VAR in which some variables are entered in logs and some are not, I have problems when trying to convert or rescale the responses such that they can be interpreted in their original u...
by Andy_T
Fri Mar 21, 2014 7:39 pm
Forum: VARs (Vector Autoregression Models)
Topic: historical decomposition in VAR with time-varying parameters
Replies: 1
Views: 5391

Re: historical decomposition in VAR with time-varying parame

Dear Todd,

I am facing the exact sam issue right now. Have you (or maybe someone else) found a practical solution to this since you posted this a few years ago?

Any suggestions would would be appreciated indeed!
by Andy_T
Thu Feb 13, 2014 12:42 pm
Forum: VARs (Vector Autoregression Models)
Topic: Factor rotation in FAVAR estimated with principal components
Replies: 0
Views: 3697

Factor rotation in FAVAR estimated with principal components

Hi, I am trying to replicate Bernanke, Boivin, Eliasz's (2005) FAVAR model but with factors extracted by principal components. When treating the federal funds rate as an observable factor and identifying the monetary policy shock recursively, BBE run a regression of the factors extracted from all in...
by Andy_T
Sun Feb 17, 2013 6:31 am
Forum: Other Time Series Analysis
Topic: VAR with time-varying parameters and stochastic volatility
Replies: 51
Views: 183330

Re: VAR with time-varying parameters and stochastic volatili

Dear Todd, in VARTVPKSC.src, STEP 4: Draw {select_draw_1,....,select_draw_T}, the states used in the KSC mixture distribution - could you please tell me why we have to divide by sqrt(var_mixdist(i)) in line 4 of the following loop? do ii = 1,nvar do vtime=stpt,endpt ewise tempinside(i) = (ystar2mat(...
by Andy_T
Thu Nov 08, 2012 7:04 am
Forum: VARs (Vector Autoregression Models)
Topic: Informative Normal-Wishart Litterman Prior
Replies: 4
Views: 7673

Re: Informative Normal-Wishart Litterman Prior

Thank you very much for pointing this out in such detail. I did indeed miss that Psi contains the variances of the respective equations such that they do not have to appear again in Omega. It all makes sense now and I can see it in the paper and in your code at comp minnprec((j-1)*lags+l) = univar(j...
by Andy_T
Wed Oct 31, 2012 10:50 am
Forum: VARs (Vector Autoregression Models)
Topic: Informative Normal-Wishart Litterman Prior
Replies: 4
Views: 7673

Re: Informative Normal-Wishart Litterman Prior

Dear Todd, thank you very much for your reply and the attached paper and code. As you pointed out Gibbs sampling is no longer needed when using the natural conjugate Normal-Wishart prior, at the cost of having to impose symmetry across equations when specifying the prior coefficient covariance matri...
by Andy_T
Thu Oct 04, 2012 10:26 am
Forum: VARs (Vector Autoregression Models)
Topic: Informative Normal-Wishart Litterman Prior
Replies: 4
Views: 7673

Informative Normal-Wishart Litterman Prior

Dear all, I am trying to replicate the Normal-Wishart Litterman Prior as in Kadiyala and Karlsson, 1997 (JAE) pp. 104-105. GIBBSVAR.src handles the Normal-Diffuse version of it, but I want the prior for the residual covariance matrix to be informative. I know that due to the natural conjugacy of the...