I am trying to generate a 1-step-ahead forecast of a quarterly variable using a monthly variable with the `midasr`

package. The trouble I am having is that I can only estimate a `MIDAS`

model when the number of monthly observations in the sample is exactly 3 times as much the number of quarterly observations.

My question is how can I forecast in the `midasr`

package when the number of monthly observations is not an exact multiple of the quarterly observations (e.g. when I have a new monthly data point that I want to use to update the forecast)?

As an example, suppose I run the following code to generate a 1-step-ahead forecast when I have `(n)`

quarterly observations and `(3*n)`

monthly observations:

```
#first I create the quarterly and monthly variables
n <- 20
qrt <- rnorm(n)
mth <- rnorm(3*n)
#I convert the data to time series format
qrt <- ts(qrt, start = c(2009, 1), frequency = 4)
mth <- ts(mth, start = c(2009, 1), frequency = 12)
#now I estimate the midas model and generate a 1-step ahead forecast
library(midasr)
reg <- midas_r(qrt ~ mls(qrt, 1, 1) + mls(mth, 3:6, m = 3, nealmon), start = list(mth = c(1, 1, -1)))
forecast(reg, newdata = list(qrt = c(NA), mth =c(NA, NA, NA)))
```

This code works fine. Now suppose I have a new monthly data point that I want to include, so that the new monthly data is:

```
nmth <- rnorm(3*n +1)
```

I tried running the following code to estimate the new model:

```
reg <- midas_r(qrt ~ mls(qrt, 1, 1) + mls(nmth, 2:7, m = 3, nealmon), start = list(mth = c(1, 1, -1))) #I now use 2 lags instead 3 with the new monthly data
```

However I get an error message saying: `'Error in mls(nmth, 2:7, m = 3, nealmon) : Incomplete high frequency data'`

I could not find anything online on how to deal with this problem. Any help is greatly appreciated.