mboldin
commented
8 months ago I have worked on cases for classical VAR modeling with similar objectives, holding one item fixed or on a fixed path and simulating forward the remainder of the VAR.
In the non Bayesian VAR case I would make a custom re-formulation of the model (not re estimating but modifying the equations) and then use a 'canned' bootstrap simulator on this model
For the Bayesian case it is harder if you want to be truly Bayesian all the way through. But there is an option to simply treat the BVAR parameters as if they came from a classical VAR and reformulate.
I would need to know more about your model to say more or how.
For your other questions, I will look at the predictive draw routine and verify it is bootstrap like. If so on each draw, the next forecast would be tied directly to the prior in a chain p(y(t+1)~|y(t)) where ~ represents a random variable, while y(t) is fixed. So everything in a full draw is connected and independently derived from any other draw.
On Sun, Feb 25, 2024, 2:16 AM RightHandOfDoom @.***> wrote:
Hello! First: thanks for this awesome package!
The first question I have a question is about the output of the predictive.draws()-function. This is my workflow:
data(usmacro.update) library(purrr) model<-bvar.sv.tvp(usmacro.update,nrep=500,nburn=50)
inf_proj<-map(1:10, function(h) predictive.draws(model,v=1, h=h)$y) une_proj<-map(1:10, function(h) predictive.draws(model,v=2,h=h)$y) ir_proj<-map(1:10, function(h) predictive.draws(model,v=3,h=h)$y)
this gives me three objects (one for each variable), which are lists with 10 entries each (one for each forecast horizon), where each list entry is 50 forecast draws at any horizon. For instance, inf_proj[[3]] is a numeric vector with 50 draws of of the first variable (inflation) three periods ahead.
The question I have is how these entries relate to earlier forecast horizons (i.e. inf_proj[[1]] and inf_proj[[2]]) as well as later ones (e.g. inf_proj[[4]], inf_proj[[5]], and so on). The reason is that I want to trace out "paths" of forecasted variables over the forecast horizon, e.g. the path a variable could take from t+1 to t+10.
The second questions I have is about the connection between predictive draws for all variables at any given horizon. For instance, how are the draws for inf_proj[[3]], une_proj[[3]], and ir_proj[[3]] related to one another, as well as to previous draws?
The reason I am asking is because in the end, I would like to produce predictive draws for the first and the second variable when the third follows a provided path. For instance, the vector c(0.022,0.025,0.0275,0.03,0.03,0.035,0.03,0.030.028,0.025).
To achieve this, my approach would have been to re-write the bvar.sv.tvp-functions such that the predictive draws for the third variable at every horizon are the same number (e.g. 50 draws of 0.022 for h=1, 50 draws of 0.025 for h=2, .. and so forth).
Any help or hint is deeply appreciated! Thank you very much for your support!
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RightHandOfDoom
Hello! First: thanks for this awesome package!
The first question I have a question is about the output of the predictive.draws()-function. This is my workflow:
data(usmacro.update) library(purrr) model<-bvar.sv.tvp(usmacro.update,nrep=500,nburn=50)
inf_proj<-map(1:10, function(h) predictive.draws(model,v=1, h=h)$y) une_proj<-map(1:10, function(h) predictive.draws(model,v=2,h=h)$y) ir_proj<-map(1:10, function(h) predictive.draws(model,v=3,h=h)$y)
this gives me three objects (one for each variable), which are lists with 10 entries each (one for each forecast horizon), where each list entry is 50 forecast draws at any horizon. For instance, inf_proj[[3]] is a numeric vector with 50 draws of of the first variable (inflation) three periods ahead.
The question I have is how these entries relate to earlier forecast horizons (i.e. inf_proj[[1]] and inf_proj[[2]]) as well as later ones (e.g. inf_proj[[4]], inf_proj[[5]], and so on). The reason is that I want to trace out "paths" of forecasted variables over the forecast horizon, e.g. the path a variable could take from t+1 to t+10.
The second questions I have is about the connection between predictive draws for all variables at any given horizon. For instance, how are the draws for inf_proj[[3]], une_proj[[3]], and ir_proj[[3]] related to one another, as well as to previous draws?
The reason I am asking is because in the end, I would like to produce predictive draws for the first and the second variable when the third follows a provided path. For instance, the vector c(0.022,0.025,0.0275,0.03,0.03,0.035,0.03,0.030.028,0.025).
To achieve this, my approach would have been to re-write the bvar.sv.tvp-functions such that the predictive draws for the third variable at every horizon are the same number (e.g. 50 draws of 0.022 for h=1, 50 draws of 0.025 for h=2, .. and so forth).
Any help or hint is deeply appreciated! Thank you very much for your support!