In practical application, I have witnessed often the following practice. One observes a pair $(x_t, y_t)$ over time. Under the assumption that they are linearly related, we regress one against the other using geometric weights rather than uniform ones, i.e., the OLS minimizes $$\sum_{t=0}^\infty k^{-t} (y_{T-t}- a x_{T-t}-b)^2$$ for some $k\in (0,1)$. This is very intuitive: we weight less observations far in the past. Compared to a "boxcar" weighting scheme, it has also the advantage of producing estimates that are changing smoothly over time, because observations do not fall abruptly off the observation window. However, I wonder if there is a probabilistic model underlying the relationship between $x_t$ and $y_t$ that justifies this choice.

In practical application, I have witnessed often the following practice. One observes a pair $(x_t, y_t)$ over time. Under the assumption that they are linearly related, we regress one against the other using geometric weights rather than uniform ones, i.e., the OLS minimizes $$\sum_{t=0}^\infty k^{t} (y_{T-t}- a x_{T-t}-b)^2$$ for some $k\in (0,1)$. This is very intuitive: we weight less observations far in the past. Compared to a "boxcar" weighting scheme, it has also the advantage of producing estimates that are changing smoothly over time, because observations do not fall abruptly off the observation window. However, I wonder if there is a probabilistic model underlying the relationship between $x_t$ and $y_t$ that justifies this choice.