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	समय-परिवर्ती पैरामीटर ऑटोरेग्रेसिव मॉडल (TVP-AR)	स्टेट स्पेस मॉडल (कलमन फिल्टर)
क्षेत्र	अर्थमिति	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	1976–2005	1990
प्रवर्तक	Cooley & Prescott (1976); further developed by Kim & Nelson (1999) and Cogley & Sargent (2001, 2005)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
प्रकार	Time-series model with drifting coefficients	State space time series model
मौलिक स्रोत	Cogley, T., & Sargent, T. J. (2005). Drifts and volatilities: Monetary policies and outcomes in the post WWII US. Review of Economic Dynamics, 8(2), 262-302.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
उपनाम	TVP-AR, time-varying AR, state-space AR with drifting coefficients, random-walk coefficient AR	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
संबंधित	4	4
सारांश	The Time-Varying Parameter Autoregressive (TVP-AR) model extends the classical AR model by allowing its autoregressive coefficients to drift over time, typically as a random walk. Cast as a state-space system, the model captures gradual structural change in the dynamics of a univariate time series without imposing a fixed break date.	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.