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A non-Bayesian, regression-based or generalized least squares (GLS)-basedapproach is formally proposed to estimate a class of time-varying AR parametermodels. This approach has partly been used by Ito et al. (2014, 2016a,b), andis proven to be efficient because, unlike conventional methods, it does notrequire Kalman filtering and smoothing procedures, but yields a smoothedestimate that is identical to the Kalman-smoothed estimate. Unlike the maximumlikelihood estimator, the possibility of the pile-up problem is negligible. Inaddition, this approach enables us to deal with stochastic volatility models,models with a time-dependent variance-covariance matrix, and models withnon-Gaussian errors that allow us to deal with abrupt changes or structuralbreaks in time-varying parameters.

An Alternative Estimation Method of a Time-Varying Parameter Model