Contemporaneous threshold autoregressive models
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Contemporaneous threshold autoregressive models estimation, forecasting, and rational expectations applications by Michael Dueker

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Published by Federal Reserve Bank of St. Louis in [St. Louis, Mo.] .
Written in English

Subjects:

  • Regression analysis.

Book details:

Edition Notes

StatementMichael Dueker, Martin Sola, and Fabio Spagnolo.
SeriesWorking paper ;, 2003-024B, Working paper (Federal Reserve Bank of St. Louis : Online) ;, 2003-024B.
ContributionsSola, Martin, 1960-, Spagnolo, Fabio, 1969-, Federal Reserve Bank of St. Louis.
Classifications
LC ClassificationsHB1
The Physical Object
FormatElectronic resource
ID Numbers
Open LibraryOL3476415M
LC Control Number2005615901

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This paper proposes a contemporaneous smooth transition threshold autoregressive model (C-STAR) as a modification of the smooth transition threshold autoregressive model surveyed in Tersvirta (), in which the regime weights depend on the ex ante probability that a latent regime-specific variable will exceed a threshold value. Dueker et al. () worked on the STAR model and proposed the Contemporaneous Smooth Threshold Autoregressive (C-STAR), model which is a modification of Teräsvirta (). This C-STAR model . autoregressive (C-MSTAR) model in which the regime weights depend on the ex ante probabilities that latent regime-specific variables exceed certain threshold values. The model is a multivariate generalization of the contemporaneous threshold autoregressive model introduced by Dueker et al. (). A key feature of the model is that the. This paper proposes a contemporaneous-threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex-ante probabilities that latent regime.

Downloadable! In this paper we propose a contemporaneous threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex ante probabilities that latent regime-specific variables exceed certain threshold values. A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data.   This paper proposes a contemporaneous smooth transition threshold autoregressive model (C-STAR) as a modification of the smooth transition threshold autoregressive model surveyed in Teräsvirta ( Modelling economic relationships with smooth transition regressions. In: Ullah, A., Giles, D.E.A. (Eds.), Handbook of Applied Economic Statistics. ear models to help resolve ongoing di culties in real data. Tong’s explanation and application of locally lin-ear threshold models introduced striking opportunities for model building strategies. 2. Threshold Autoregressive (TAR) Models The Threshold Autoregressive (TAR) family pro-posed and explained by Tong () are contained. TAR and STAR models typically associate different regimes with small and large values of the transition variables and are capable of characterizing state-dependent interactions among the variables. This paper contributes to the literature on multivariate nonlinear models by proposing a contemporaneous-threshold multivariate STAR, or C-MSTAR, model.

The C-STGARCH model is a generalization to second conditional moments of the contemporaneous smooth transition threshold autoregressive model of Dueker et al. () in which the regime weights depend on the ex ante probability that a contemporaneous latent regime-specific variable exceeds a threshold value. Abstract. This paper proposes a contemporaneous smooth transition threshold autoregressive model (C-STAR) as a modification of the smooth transition threshold autoregressive model surveyed in Teräsvirta (), in which the regime weights depend on the ex ante probability that a latent regime-specific variable will exceed a threshold value. series. Tong () develops a threshold autoregressive (TAR) model and uses it to predict stock price movements. A number of new models have been proposed since the seminal work of Tong (), including the smooth transition threshold autoregressive model (STAR) of Chan and Tong () and the functional-coefficient autoregressive (FAR) model of Chen and Tsay (). Tsay () . multiple thresholds by Gonzalo and Pitarakis [32], and to models with endogenous regressors by Caner and Hansen [18]. To improve on Hansen’s asymptotic approximation, GonzaloandWolf[34]proposeasubsamplingprocedure. The least-squares estimate of the threshold has a non-standard asymptotic distribution. As an alternative, Seo.