# Converting monthly data to quarterly

I have monthly shock series, which I want to convert to quarterly form. I have seen several methods like taking average of 3 months or summing 3 months for making a quarter. I would like to know how I can know which method is useful for me. Taking average or summing? Or is it something about personal choice in research methodology?

More details:

I intend to get impulse responses of real GDP to news shocks (which are already available). My GDP series are in quarterly form and my shock series in monthly form. So I need to convert shock series to quarterly form and do local projections.

• Hi, could you please provide a bit more details and clarity in your Q about the problem? This will be different for different variables. If we are talking about total units of good Q produced by company X summing then would be appropriate, if we are talking about growth it would be more appropriate to do average and so on... choice on how to aggregate data is not a la carte but it depends on situation and context
– 1muflon1
Apr 4, 2021 at 20:36
• @1muflon1 Thanks. I have edited my question. Apr 4, 2021 at 20:48

If $$y_{t+h} = \alpha^h + \beta_h news\_shock_t + \sum_{j=1}^{L}\delta_j^hx_{t-j} + \dots + \sum_{j=1}^{L}\gamma_j^hy_{t-j} + \epsilon^h$$ is the OLS for the h-th horizon, adding the shocks up is going to yield a $$\beta_h$$ that is exactly three times smaller than the $$\beta_h$$ you will obtain by averaging. The statistical significance will not be affected.