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View Code? Open in Web Editor NEWmidasml package is dedicated to run predictive high-dimensional mixed data sampling models
midasml package is dedicated to run predictive high-dimensional mixed data sampling models
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?
Hi,
can we use the "midaslm"package to predict a monthly data using a daily data?
thank you.
T
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 ๏ผ
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
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
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?
a typo in the foreach line:
outlist <- foreach(i = seq(nfolds), .packages = c("midsaml")) %dopar%{
->
outlist <- foreach(i = seq(nfolds), .packages = c("midasml")) %dopar%{
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.
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.
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!
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