Premium
STATE‐DEPENDENT MODELS: A GENERAL APPROACH TO NON‐LINEAR TIME SERIES ANALYSIS
Author(s) -
Priestley M. B.
Publication year - 1980
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1980.tb00300.x
Subject(s) - autoregressive model , mathematics , bilinear interpolation , setar , linear model , star model , series (stratigraphy) , class (philosophy) , state (computer science) , exponential function , nonlinear autoregressive exogenous model , time series , econometrics , autoregressive integrated moving average , algorithm , statistics , computer science , artificial intelligence , mathematical analysis , paleontology , biology
. We construct a general class of non‐linear models, called ‘state‐dependent models’, which have a very flexible non‐linear structure and which contain, as special cases, bilinear, threshold autoregressive, and exponential autoregressive models. We describe a sequential type of recursive algorithm for identifying state‐dependent models, and show how such models may be used for forecasting and for indicating specific types of non‐linear behaviour.