# What does it mean to 'align data frequencies'?

If I have two variables that have different sampling frequencies, one of the first steps is to align the frequencies; could someone explain the intuition of this alignment without being too technical. I am reading some theory on doing MIDAS regression and can't grasp this concept.

What does it mean to align data frequencies? for my case; I have a lower frequency variable Yt which is sampled monthly, and one higher frequency variable that is sampled daily.

For my purposes I need only one explanatory variable, so my mapping should only require the first row of the matrix.

A simple example would help me a lot!

Thank you

Well in this particular document (of which I am one of the authors and from which the excerpt was taken) aligning time-series means simply forming the matrix $\mathbf{X}$. Here is the simple example. Let $y_t$ be a quarterly time series and $x_\tau$ be a monthly time series. Suppose we are interested in a model, where we try to explain what happens to $y$ in one quarter using the the monthly data in that quarter. So for quarter $t=1$ we have the monthly data $\tau=1,2,3$. Then the MIDAS regression model is the following:
$$y_t=x_{3t}\beta_0+x_{3t-1}\beta_1+x_{3t-2}\beta_2+\varepsilon_t$$
In this case we aligned high frequency variable $x_\tau$ into a vector $X_t=[x_{3t},x_{3t-1},x_{3t-2}]$. This operation can be called embedding, or another term.