A non-Bayesian,  generalized least squares (GLS)-based approach is formally proposed to estimate a class of time-varying AR parameter models. This approach has partly been used by Ito et al. (2014, 2016a,b), and is proven very efficient because, unlike conventional methods, it does not require the Kalman filtering and smoothing procedures, but yields a smoothed estimate that is identical to the Kalman-smoothed estimate. Unlike the maximum likelihood estimator, the possibility of the pile-up problem is shown to be small . In addition, this approach enables us to possibly deal with stochastic volatility models and models with a time-dependent variance-covariance matrix  .
A non-Bayesian, regression-based or generalized least squares (GLS)-based approach is formally proposed to estimate a class of time-varying AR parameter models. This approach has partly been used by Ito et al. (2014, 2016a,b), and is proven to be efficient because, unlike conventional methods, it does not require Kalman filtering and smoothing procedures, but yields a smoothed estimate that is identical to the Kalman-smoothed estimate. Unlike the maximum likelihood estimator, the possibility of the pile-up problem is negligible . In addition, this approach enables us to deal with stochastic volatility models , models with a time-dependent variance-covariance matrix , and models with non-Gaussian errors that allow us to deal with abrupt changes or structural breaks in time-varying parameters .
A
regression-based or
R
very
to be
D
the
R
shown to be small
negligible
D
possibly
R
and
,
A
, and models with non-Gaussian errors that allow us to deal with abrupt changes or structural breaks in time-varying parameters