Comments (3)
step <- midas_r(y~mls(y,1,1)+mls(xx,0:3,3,polystep),start = list(xx=list(c(1,2,3,4),3,3,a=c(2,2,2)))), for example, i tried this,and error:Error in polystep(c(xx1 = 1, xx2 = 2, xx3 = 3, xx4 = 4), 4L, 3) :
argument "a" is missing, with no default
came out, how to set the starting value properly?
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You should supply a into mls, not in start. The following should work:
step <- midas_r(y~mls(y,1,1)+mls(xx,0:3,3,polystep,a=c(2,2,2)),start = list(xx=list(c(1,2,3,4))))
Starting values are only needed for the parameters of weight function which are optimized. In step function parameter a is fixed.
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Thanks!
I encountered a new error when using average_forecast(), like this,
avgf09.fixed <- average_forecast(list(nealmon09,almonp09,beta0.09,betan.09,step.09),data = list(y=y,xx=xx),
insample = 1:47,outsample = 48:50,
type = 'fixed',
measures = c("MSE","MAPE","MASE"),
fweights=c("EW","BICW","MSFE","DMSFE"))
Error in midas_r.fit(prepmd) :
The optimisation algorithm of MIDAS regression failed with the following message:
Error in if (sum(nb) < eps) { : missing value where TRUE/FALSE needed
Please try other starting values or a different optimisation function
how to solve this ?
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Related Issues (20)
- Error in hAh_test() HOT 4
- Contribution variables
- fitted value of in-sample based on average_forecast()
- Issue with average_forecast HOT 1
- Variable lengths differ (trend) HOT 1
- Stationarity - Transformations for Mixed Frequency Data HOT 1
- markov-switching midas models HOT 3
- error when use select_and_forecast HOT 1
- Impove mlsd to work correctly with dates
- Add forecasting to midas_sp, midas_nlpr, midas_mmm and midas_lstr
- Write proper documentation for non-parametric models
- Lint the package
- Regarding k in the mls() HOT 1
- Average Forecast Combination Weight Assignment HOT 2
- Consistent warning messages in the midasr package and unrealistic MSE in and out sample values for a regression. HOT 1
- incorrect average_forecast values for midas_regressions
- Clarifications for select_and_forecast
- Query regarding multiple one-step ahead forecasts
- A Question about Model
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