Non-linear Time Series: A Selective Review*

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Sufficient Forecasting Using Factor Models (2015, background): “Yet, nonlinear models usually outperform linear models in time series analysis (Tjøstheim, 1994), especially in multi-step prediction.”
A simulation study of artificial neural networks for nonlinear time-series forecasting (2001, background): “During the past two decades, a number of nonlinear time-series models such as the bilinear model [6], the threshold autoregressive (TAR) model [7], the smoothing transition autoregressive (STAR) model [8], and the autoregressive conditional heteroscedastic (ARCH) model [9] have been developed (see D”
Partially Linear Models (2000, background): “See Chapter 48 of Teräsvirta, Tjøstheim and Granger (1994) for more details.”
DIAGNOSTIC CHECKING FOR THE ADEQUACY OF NONLINEAR TIME SERIES MODELS (2003, background): “…been advancing rapidly ~e+g+, Subba Rao and Gabr, 1984; Priestley, 1988; Tong, 1990; Brock, Hsieh, and LeBaron, 1991; Granger and Teräsvirta, 1993; Tjøstheim, 1994; Teräsvirta, Tjøstheim, and Granger, 1994; Terdik, 1999!+ In modern time series analysis, one often considers the data generating…”
Functional Coefficient Regression Models for Non‐linear Time Series: A Polynomial Spline Approach (2004, unknown): “Key words: forecasting, functional autoregressive model, non-parametric regression, threshold autoregressive model, varying coefficient model”