手法を比較

	時変係数OLS（TVP-OLS）	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 (1976); further developed by Harvey (1990)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
種類	Time-series regression with evolving 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-OLS, time-varying coefficient regression, rolling OLS, locally weighted OLS	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
関連	4	4
概要	Time-Varying Parameter OLS extends classical ordinary least squares to allow regression coefficients to change over time. Instead of assuming fixed slopes throughout the sample, the model treats each coefficient as a stochastic process, tracking how economic relationships evolve — making it well-suited for analysing structural change 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.