Title: An alternative estimation method for time-varying parameter models
Authors: Tatsuma Wada - Keio University (Japan) [presenting]
Mikio Ito - Keio University (Japan)
Akihiko Noda - Kyoto Sangyo University (Japan)
Abstract: 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 previously, 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.