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wbnicholson / BigVAR

Dimension Reduction Methods for Multivariate Time Series
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predict() failed but the source code in BigVARObjectClass.R can work #18

Closed Sue-17 closed 3 years ago

Sue-17 commented 3 years ago

Hi @wbnicholson, Thank you for developing this valuable package! I would really appreciate some help with the BigVAR::predict function.

Package Version: the GitHub version (1.0.7) instead of the CRAN version(1.0.6)

Data: I have a matrix of 108 quarterly time series (call it train_data). Each of them has 24 points. I also created a subset of 15 time series for testing purpose (call it sub_train_data). In addition, I added 3 season dummies.

Model Setup: varx_list=list() varx_list$k = ncol(train_data) - 3 # number of equations (variables) = 111 - 3 = 108 varx_list$s = 4 lasso.var = constructModel(Y=train_data, p=4, struct='Basic', gran=c(50, 10), VARX=varx_list, RVAR=F, h=1, cv='LOO', MN=F, verbose=F, IC=T) results=cv.BigVAR(lasso.var) BigVAR::predict(results, n.ahead=1)

Issue: the predict function worked fine on the sub_train_data, but threw out an error for the train_data: "Error in if (contemp) { : argument is of length zero". (As expected, the model with larger data contains more 0 coefficients, but I am not sure how that affects the predict function.)

Investigate: I focused on n.ahead=1 because I could not trace the VARXCons function to test n.ahead>1. I found that, if I create a udf directly using the source code (line 2094 of BigVARObjectClass.R), this udf can make predictions.

Questions:

  1. the source code can work but why does it fail when I use BigVAR::predict()?
  2. the BigVAR::predict() can work on a small data but why does it fail when I use a larger data?
Sue-17 commented 3 years ago

I realized that it is because the virtual machine I used loads in the CRAN version of the package by default. After adding the detaching command at the beginning, I was able to switch to the GitHub version.