Detecting Relevant Changes in Time Series Models

Dette, Holger; Wied, Dominik

doi:10.2139/ssrn.2417634

Cited by 22 publications

(40 citation statements)

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“…Our test shares some similarity with the work of Dette and Wied (2016), who consider CUSUM tests in the spirit of Brown et al (1975) but allow for a constant parameter differences under the null. They do, however, not consider local-to-zero breaks, which would eliminate break date uncertainty in our asymptotic framework.…”

Section: Introductionmentioning

confidence: 89%

Does modeling a structural break improve forecast accuracy?

Boot

Pick

2020

Journal of Econometrics

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Section: Introductionmentioning

confidence: 89%

Does modeling a structural break improve forecast accuracy?

Boot

Pick

2020

Journal of Econometrics

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show abstract

“…Building on Theorem 2, and inspired by an idea by Dette and Wied (2016), it is possible to propose a test for "relevant" randomness. As mentioned in the introduction, it is possible that a small amount of randomness in the autoregressive coefficient (measured as τ 2 ) may actually be negligible for practical purposes, with a standard and simpler AR (1) model working better than the RCA(1) model.…”

Section: Testing Formentioning

confidence: 99%

“…Thanks to the self-normalised nature of the WLS estimator, our test is robust to the stationarity or lack thereof of X t , and no prior knowledge of this is required; also, our test is not affected by the well-known inconsistency of the estimator of σ 2 when X t is nonstationary (see Aue and Horváth, 2011). In addition to this, motivated by a recent paper by Dette and Wied (2016), we also study a test for "relevant" randomness as an application of our results on the limiting distribution of the WLS estimator of τ 2 .…”

Section: Introductionmentioning

confidence: 99%

Testing for randomness in a random coefficient autoregression model

Horváth

Trapani

2019

Journal of Econometrics

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We propose a test to discern between an ordinary autoregressive model, and a random coefficient one. To this end, we develop a full-fledged estimation theory for the variances of the idiosyncratic innovation and of the random coefficient, based on a two-stage WLS approach. Our results hold irrespective of whether the series is stationary or nonstationary, and, as an immediate result, they afford the construction of a test for "relevant" randomness. Further, building on these results, we develop a randomised test statistic for the null that the coefficient is non-random, as opposed to the alternative of a standard RCA(1) model. Monte Carlo evidence shows that the test has the correct size and very good power for all cases considered.

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“…High dimensional time series change-point detection problems are the obvious but by no means straightforward extension of the univariate case. To detect changes in the covariance matrix of a multivariate time series, Aue et al (2009) introduced a method using a nonparametric CUSUM type test, Dette & Wied (2016) proposed a test where the dimension of the data is fixed while more recently Kao et al (2018) considered the case where the dimension of the data increases with the sample size (they also investigated change-point analysis based on Principal Component Analysis). Sundararajan & Pourahmadi (2018) proposed a new method for detecting multiple change-points in the covariance structure of a multivariate piecewise-stationary process.…”

Section: Introductionmentioning

confidence: 99%

Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity

Anastasiou

Cribben

Fryźlewicz

2020

Preprint

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Evidence of the non stationary behavior of functional connectivity (FC) networks has been observed in task based functional magnetic resonance imaging (fMRI) experiments and even prominently in resting state fMRI data. This has led to the development of several new statistical methods for estimating this time-varying connectivity, with the majority of the methods utilizing a sliding window approach. While computationally feasible, the sliding window approach has several limitations. In this paper, we circumvent the sliding window, by introducing a statistical method that finds change-points in FC networks where the number and location of change-points are unknown a priori. The new method, called cross-covariance isolate detect (CCID), detects multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. CCID allows for change-point detection in the presence of frequent changes of possibly small magnitudes, can assign change-points to one or multiple brain regions, and is computationally fast. In addition, CCID is particularly suited to task based data, where the subject alternates between task and rest, as it firstly attempts isolation of each of the change-points within subintervals, and secondly their detection therein. Furthermore, we also propose a new information criterion for CCID to identify the change-points. We apply CCID to several simulated data sets and to task based and resting state fMRI data and compare it to recent change-point methods. CCID is also applicable to electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. Similar to other biological networks, understanding the complex network organization and functional dynamics of the brain can lead to profound clinical implications. Finally, the R package ccid implementing the method from the paper is available from GitHub.

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Detecting Relevant Changes in Time Series Models

Cited by 22 publications

References 0 publications

Does modeling a structural break improve forecast accuracy?

Does modeling a structural break improve forecast accuracy?

Testing for randomness in a random coefficient autoregression model

Cross-covariance isolate detect: a new change-point method for estimating dynamic functional connectivity

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