手法を比較

	時変パラメータGLS (TVP-GLS)	状態空間モデル（カルマンフィルタ）
分野	計量経済学	計量経済学
系統	Regression model	Regression model
提唱年	1976	1990
提唱者	Cooley & Prescott	Harvey; Durbin & Koopman (state space treatment); Kalman filter
種類	Time-series regression with drifting coefficients	State space time series model
原典	Cooley, T. F., & Prescott, E. C. (1976). Estimation in the presence of stochastic parameter variation. Econometrica, 44(1), 167–184.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
別名	TVP-GLS, time-varying coefficient GLS, adaptive GLS, state-space GLS	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
関連	2	4
概要	Time-varying parameter GLS extends generalized least squares to settings where regression coefficients are not fixed constants but evolve over time according to a stochastic process. By embedding the model in a state-space framework and applying GLS corrections for non-spherical errors, it captures structural change, regime shifts, and gradually drifting relationships in time-series data.	A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases.