Bootstrap order selection for SETAR models

Fenga, Livio; Politis, Dimitris N.

doi:10.1080/00949655.2013.811507

Cited by 4 publications

(1 citation statement)

References 54 publications

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“…A suitable version of the AIC, called pooled-AIC -employed in different applications, e.g. in [24] and [25] -will be used in the empirical study. Its estimation is conditional upon both a specified threshold delay and a fixed threshold value.…”

Section: Fengamentioning

confidence: 99%

A wavelet threshold denoising procedure for multimodel predictions: An application to economic time series

Fenga

2017

Statistical Analysis

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Noise-affected economic time series, realizations of stochastic processes exhibiting complex and possibly non-linear dynamics, are dealt with. This is often the case of time series found in economics, which notoriously suffer from problems such as low signal-to-noise ratios, asymmetric cycles and multi-regimes patterns. In such a framework, even sophisticated statistical models might generate suboptimal predictions, whose quality can further deteriorate unless time consuming updating or deeper model revision procedures are carried out on a regular basis. However, when the models' outcomes are expected to be disseminated in timeliness manner (as in the case of Central Banks or national statistical offices), their modification might not be a viable solution, due to time constraints. On the other hand, if the application of simpler linear models usually entails relatively easier tuning-up procedures, this would come at the expenses of the quality of the predictions yielded. A mixed, selftuning forecasting method is therefore proposed. This is an automatic, two-stage procedure, able to generate predictions by exploiting the denoising capabilities provided by the wavelet theory in conjunction with a compounded forecasting generator. Its out-of-sample performances are evaluated through an empirical study carried out on macroeconomic time series.

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Section: Fengamentioning

confidence: 99%

A wavelet threshold denoising procedure for multimodel predictions: An application to economic time series

Fenga

2017

Statistical Analysis

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Linear approximation of the Threshold AutoRegressive model: an application to order estimation

2022

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This paper proposes a linear approximation of the nonlinear Threshold AutoRegressive model. It is shown that there is a relation between the autoregressive order of the threshold model and the order of its autoregressive moving average approximation. The main advantage of this approximation can be found in the extension of some theoretical results developed in the linear setting to the nonlinear domain. Among them is proposed a new order estimation procedure for threshold models whose performance is compared, through a Monte Carlo study, to other criteria largely employed in the nonlinear threshold context.

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Predictive density criterion for SETAR models

Hamdi

Khalfi

2019

Communications in Statistics - Simulation and Computation

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Bootstrap order selection for SETAR models

Cited by 4 publications

References 54 publications

A wavelet threshold denoising procedure for multimodel predictions: An application to economic time series

A wavelet threshold denoising procedure for multimodel predictions: An application to economic time series

Linear approximation of the Threshold AutoRegressive model: an application to order estimation

Predictive density criterion for SETAR models

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