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pro vyhledávání: '"Giannerini, Simone"'
Many macroeconomic time series are characterised by nonlinearity both in the conditional mean and in the conditional variance and, in practice, it is important to investigate separately these two aspects. Here we address the issue of testing for thre
Externí odkaz:
http://arxiv.org/abs/2308.00444
Threshold autoregressive moving-average (TARMA) models are popular in time series analysis due to their ability to parsimoniously describe several complex dynamical features. However, neither theory nor estimation methods are currently available when
Externí odkaz:
http://arxiv.org/abs/2211.08205
The Misspecification-Resistant Information Criterion (MRIC) proposed in [H.-L. Hsu, C.-K. Ing, H. Tong: On model selection from a finite family of possibly misspecified time series models. The Annals of Statistics. 47 (2), 1061--1087 (2019)] is a mod
Externí odkaz:
http://arxiv.org/abs/2202.09225
We consider bootstrap-based testing for threshold effects in non-linear threshold autoregressive (TAR) models. It is well-known that classic tests based on asymptotic theory tend to be oversized in the case of small, or even moderate sample sizes, or
Externí odkaz:
http://arxiv.org/abs/2201.00028
Publikováno v:
In Journal of Econometrics February 2024 239(1)
We present supremum Lagrange Multiplier tests to compare a linear ARMA specification against its threshold ARMA extension. We derive the asymptotic distribution of the test statistics both under the null hypothesis and contiguous local alternatives.
Externí odkaz:
http://arxiv.org/abs/2103.13977
Regulation is an important feature characterising many dynamical phenomena and can be tested within the threshold autoregressive setting, with the null hypothesis being a global non-stationary process. Nonetheless, this setting is debatable since dat
Externí odkaz:
http://arxiv.org/abs/2002.09968
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Autor:
Giannerini, Simone1 (AUTHOR), Goracci, Greta2 (AUTHOR) greta.goracci@unibz.it
Publikováno v:
Mathematics (2227-7390). Feb2023, Vol. 11 Issue 3, p757. 27p.