ARIMA model, daily data, weekly external regressor I have a data set of daily values that I would like to fit an ARIMA model.  I also have an additional data set of weekly values that I believe would serve well as an external regressor in the ARIMA model.  From my understanding, the regressor must have the same length as the primary time series.  I am wondering how to handle a situation like this.  Would it make more sense to aggregate the daily data to weekly, somehow expand the weekly data to the daily level (e.g. repeat values), or perhaps a different approach entirely?  For what it may be worth, I am generally using R and the forecast package.
If we extend the question a little further, let's say there are two external regressors, one at the daily level and another at the weekly.  Would the approach differ here as well?
 A: If external values are sum of days in week, then having same values at day level would will be incorrect. If weekly values are like avg temp etc then these can be kept same for all days of week ( Not completely correct).  
It's better if you aggregate daily external variables and make them weekly.
Most importantly it depends on your requirement. If weekly forecasting prediction is acceptable instead of weekly go with 2nd approach.
A: Since daily data is by it's nature more reproducible i.e. we repeat activity based upon day of the week and to a much lesser degree week of the year while also including holiday effects I would suggest predicting daily values and then (if needed ) obtain aggregate forecasts. weekly values are distorted by holiday effects which don't a;way fall in the same week of the year . Using the "same value" across 7 days for a predictor is not necessarily bad just think of a design matrix of 6 daily dummies and 51 weekly dummies. You might look at  http://www.autobox.com/cms/index.php/afs-university/intro-to-forecasting/doc_download/53-capabilities-presentation .. slide 49-68 .
Aggregated forecasts and their uncertainties are easily formed by using monte-carlo techniques. Daily models enable rapid updating of weekly forecasts. Additionally the 53 week leap years distorts weekly effects. Avoid weekly when you can !
