مقایسهٔ روش‌ها

	تحلیل داده‌های تابلویی با پارامترهای متغیر در طول زمان	مدل فضای حالت (فیلتر کالمن)
حوزه	اقتصادسنجی	اقتصادسنجی
خانواده	Regression model	Regression model
سال پیدایش	1960–2003	1990
پدیدآور	Cheng Hsiao (panel treatment); Kalman (state-space foundation)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
نوع	Dynamic panel model	State space time series model
منبع بنیادین	Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
نام‌های دیگر	TVP panel model, time-varying coefficient panel model, state-space panel regression, random coefficient panel model	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
مرتبط	5	4
خلاصه	Time-varying parameter (TVP) panel data analysis extends standard panel regression by allowing the slope coefficients to evolve over time for each unit. Instead of assuming a single fixed or random coefficient, the model lets each unit's relationship between predictors and outcome shift period by period, capturing structural change, learning effects, and heterogeneous dynamics across individuals and time.	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.