  We revisit a model for time-varying linear regression that assumes the
unknown parameters evolve according to a linear dynamical system.
Counterintuitively, we show that when the underlying dynamics are stable the
parameters of this model can be estimated from data by combining just two
ordinary least squares estimates. We offer a finite sample guarantee on the
estimation error of our method and discuss certain advantages it has over
Expectation-Maximization (EM), which is the main approach proposed by prior
work.
