Modelling nonlinear economic time series

By Timo Teräsvirta, Dag Tjøstheim, Clive W. J. Granger. 2010

325 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/de6f0153ddacf53d819b2158a515eb42ac802e30

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Citations in the corpus

Machine Learning Advances for Time Series Forecasting (2020, unknown): “…solution is to restrict the function space, as for instance, imposing linearity or specific forms of parametric nonlinear models as in, for example, Teräsvirta (1994), Suarez-Fariñas et al. (2004), or McAleer and Medeiros (2008); see also Teräsvirta et al. (2010) for a recent review of such models.”
Count Time Series Models (2012, background): “Such examples are provided by the class of smooth transition autoregressive models of which the exponential autoregressive model is a special case (cf. Teräsvirta et al. (2010)).”
Neural Granger Causality (2021, background): “Model-based methods may fail in real world cases when the relationships between the past of one series and future of another falls outside of the model class [18], [19], [20].”
Threshold Regression With a Threshold Boundary (2020, methodology): “Fifth, the boundary setup of our model can be used in an alternative of threshold regression—smooth transition regression (see Teräsvirta 1998; Teräsvirta, Tjøstheim, and Granger 2010; van Dijk, Teräsvirta, and Franses 2002 for surveys).”
A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting (2016, unknown): “Digital Object Identifier 10.1109/TNNLS.2015.2411629 it could switch between two or more different regimes (for example, economic expansion and recession) [2].”
Nonparametric Estimation and Forecasting for Time-Varying Coefficient Realized Volatility Models (2018, methodology): “As in Teräsvirta, Tjøstheim, and Granger (2010), we let the covariates Yt−k , for k = 1, 5, 22 in the dynamic autoregression be invariant when generating bootstrap samples, although other bootstrap methods may also be applicable in our setting.”
A flexible semiparametric forecasting model for time series (2015, methodology): “Various nonparametric and semiparametric models, such as additive models, varying coefficient models, partially linear models, have been studied to deal with the curse of dimensionality problem in the literature (see, for example, Fan and Yao 2003, Li and Racine 2007, Teräsvirta et al 2010).”
Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques (2016, background): “For discussions, see for example Ter¨asvirta (2006), Ter¨asvirta et al. (2010, Chapter 14), or Kock and Ter¨asvirta (2011a).”