https://en.wikipedia.org/wiki/Kalman_filter

Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by using Bayesian inference and estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, one of the primary developers of its theory.

The EViews sspace (state space) object provides a straightforward, easy-to-use interface for specifying, estimating, and working with the results of your single or multiple equation dynamic system. EViews provides a wide range of specification, filtering, smoothing, and other forecasting tools which aid you in working with dynamic systems specified in state space form.

To test whether the SM–CC relationship weakened during the post-dotcom crash relative to the