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	Modèle autorégressif à paramètres variant dans le temps (TVP-AR)	Modèle ARIMA (Modèle Autorégressif Intégré à Moyenne Mobile)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1976–2005	1970
Auteur d'origine	Cooley & Prescott (1976); further developed by Kim & Nelson (1999) and Cogley & Sargent (2001, 2005)	George Box and Gwilym Jenkins
Type	Time-series model with drifting coefficients	Time series forecasting model
Source fondatrice	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.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Alias	TVP-AR, time-varying AR, state-space AR with drifting coefficients, random-walk coefficient AR	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Apparentées	4	6
Résumé	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.	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.