jstriaukas / midasml Goto Github PK

Hi,

can we use the "midaslm"package to predict a monthly data using a daily data?

thank you.

T

Hi Dr. Striaukas,

in my lasso midas regression I try to explain quarterly gdp with monthly and daily data. Now I want apply reg.sgl and I realized that it is not possible to use covariates with different frequencies. Because T in matrix x (in your reg.sgl function) is different for daily and monthly data. Can I add more than one matrix x oder did I have overseen something? Appreciate your help.

Kind Regards

Hi,

I am interested in applying this approach to one of my research papers. Sadly, I have not found a tutorial on how to use your package. Is there any example code/project available that I could use to understand better how to apply your R code?

Thank you very much!

Best,
Jonathan

a typo in the foreach line:

outlist <- foreach(i = seq(nfolds), .packages = c("midsaml")) %dopar%{

->

outlist <- foreach(i = seq(nfolds), .packages = c("midasml")) %dopar%{

In paragraph 2.4 of "High-Dimensional Granger Causality Tests with an Application to VIX and News", a null hypothesis is tested. Is the Wald statistic discussed on the same page available in midas-ml?

Hello,
I am trying to using the MIDAS model to analyze the panel data. But now I have some problems about it and I am wondering if you could give me some advice.
Does your midasml package has a function for panel data?
If so, Can you share me some relevant codes or examples ？

Hello,

Thank you for your great package! I wonder how I can accomplish the dynamic forecasting: that is, estimates for the target variable at the times it was not observed, for example on the daily level.

Thank you!

standardize = TRUE in sglfit leads to unexpected output.

Thanks to Marie Ternes for spotting this.

NOTE: using sg-MIDAS-LASSO model, covariates should be standardized at the original frequency leading to standardize = FALSE.

The explanation of "gindex" is p by 1 vector indicating group membership of each covariate. But I failed to understand how to set it accurately, could you give me some suggestions?

Fix: prox_sgl - stop after a certain number of iterations, return the non-convergence flag.
comment: if the min problem is non-converging (e.g. data irregularities) prox_sgl will not escape the loop.

Fix: sglfit - add data check-ups.
comment: if y,x has NA, Inf, stop. if y,x has large outliers, report a warning.