Solved – Econometric Model and deciding the frequency of data collection

I am looking to build an econometric model and I am wondering if using annual data vs monthly or quarterly data is going to produce a less accurate model. If the dependent variable is affected by seasonality, would it be innappropriate to use monthly or quarterly input data? Or is it still generally better to use a higher frequency of input data regardless? I would prefer to use monthly or quarterly inputs but I am worried this would skew my model. Any suggestions or insight would be greatly appreciated.

More data (collected at higher frequencies) is not really more data as higher levels of auto-correlation exist. What I have recommended is a two-fold approach 1) At what frequency would you like to detect recent unusual activity or in other words what frequency do you wish to make forecasts i.e. end of day , end of week , end of month .,etc. Once you have determined that then consider higher frequencies from that point. So if you had said quarterly as the answer to the first question , you now have to assess/specify what forecast length you are concerned about. For example you might select/specify 1 quarter. Now you can use monthly data to predict the next quarter OR daily data to predict the next quarter or hourly data to predict the next quarter. These three different approaches can all be used and you can compute the accuracy of each and then select the winner from this 1 origin. Make sure that you repeat this experiment for a few origins not just 1.

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