ScholarGate
Assistente

Confronta i metodi

Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.

Modello Autoregressivo Non Lineare (NAR)×Modello AR con rottura strutturale×
CampoEconometriaEconometria
FamigliaRegression modelRegression model
Anno di origine1978-19901989-2003
IdeatoreTong, H. (threshold AR); Terasvirta, T. (STAR variant)Perron (1989); Bai & Perron (1998, 2003)
TipoNonlinear time series modelTime-series model with structural change
Fonte seminaleTong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522201Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1-22. DOI ↗
AliasNAR model, nonlinear autoregression, NLAR, threshold autoregressive modelAR model with structural change, breakpoint AR model, piecewise autoregressive model, AR model with regime shifts
Correlati66
SintesiThe Nonlinear AR model extends the classical autoregressive framework by allowing the mapping from past values to the current value to follow an arbitrary or regime-switching nonlinear function. Major families include the Self-Exciting Threshold AR (SETAR), Smooth Transition AR (STAR), and neural network AR, each capturing different forms of asymmetry, regime shifts, or smooth nonlinear dynamics in univariate time series.The structural break AR model extends the standard autoregressive framework by allowing the intercept and autoregressive coefficients to shift at one or more unknown break dates. Each regime between consecutive break points is governed by its own AR parameters, capturing abrupt changes in the dynamics of a time series caused by crises, policy shifts, or other shocks.
ScholarGateInsieme di dati
  1. v1
  2. 2 Fonti
  3. PUBLISHED
  1. v1
  2. 2 Fonti
  3. PUBLISHED

Vai alla ricerca Scarica le diapositive

ScholarGateConfronta i metodi: Nonlinear AR Model · Structural Break AR Model. Consultato il 2026-06-17 da https://scholargate.app/it/compare