Change point tests in functional factor models with application to yield curves

Bardsley, Patrick; Horváth, Lajos; Kokoszka, Piotr; Young, Gabriel

doi:10.1111/ectj.12075

Cited by 18 publications

(28 citation statements)

References 36 publications

(74 reference statements)

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“…Change point modeling in aging research is a new and promising avenue for detecting the onset of preclinical and terminal accelerations in cognitive and neurological decline. These models have been extensively used in other research areas as well, including economics (Bardsley, Horváth, Kokoszka, & Young, 2017), climate science (Reeves, Chen, Wang, Lund, & Lu, 2007), and biostatistics (Erdman & Emerson, 2008). Many applications of change points modeling in other fields involve far denser data than the cohort data available in longitudinal aging research; however, the onset of new technologies into longitudinal aging research (e.g., mobile cognitive testing) may bridge the methods used across fields.…”

Section: Resultsmentioning

confidence: 99%

When does cognitive decline begin? A systematic review of change point studies on accelerated decline in cognitive and neurological outcomes preceding mild cognitive impairment, dementia, and death.

Karr¹,

Graham²,

Hofer³

et al. 2018

Psychology and Aging

104

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Older adults who ultimately develop dementia experience accelerated cognitive decline long before diagnosis. A similar acceleration in cognitive decline occurs in the years before death as well. To evaluate preclinical and terminal cognitive decline, past researchers have incorporated change points in their analyses of longitudinal data, identifying point estimates of how many years prior to diagnosis or death that decline begins to accelerate. The current systematic review aimed to summarize the published literature on preclinical and terminal change points in relation to mild cognitive impairment (MCI), dementia, and death, identifying the order in which cognitive and neurological outcomes decline and factors that modify the onset and rate of decline. A systematic search protocol yielded 35 studies, describing 16 longitudinal cohorts, modeling change points for cognitive and neurological outcomes preceding MCI, dementia, or death. Change points for cognitive abilities ranged from 3-7 years prior to MCI diagnosis, 1-11 years prior to dementia diagnosis, and 3-15 years before death. No sequence of decline was observed preceding MCI or death, but the following sequence was tentatively accepted for Alzheimer's disease: verbal memory, visuospatial ability, executive functions and fluency, and last, verbal IQ. Some of the modifiers of the onset and rate of decline examined by previous researchers included gender, education, genetics, neuropathology, and personality. Change point analyses evidence accelerated decline preceding MCI, dementia, and death, but moderators of the onset and rate of decline remain ambiguous due to between-study modeling differences, and coordinated analyses may improve comparability across future studies. (PsycINFO Database Record

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Section: Resultsmentioning

confidence: 99%

When does cognitive decline begin? A systematic review of change point studies on accelerated decline in cognitive and neurological outcomes preceding mild cognitive impairment, dementia, and death.

Karr¹,

Graham²,

Hofer³

et al. 2018

Psychology and Aging

104

View full text Add to dashboard Cite

show abstract

“…The literature on change point analysis for the two classes of modeling paradigms previously mentioned is rather sparse. Bardsley et al (2017) developed tests for the presence of change points in functional factor models motivated by modeling the yield curve of interest rates, while Barigozzi et al (2018) employed the binary segmentation procedure for detecting and identifying the locations of multiple change points in factor models.…”

Section: Introductionmentioning

confidence: 99%

Multiple Change Points Detection in Low Rank and Sparse High Dimensional Vector Autoregressive Models

Bai

Safikhani

Michailidis

2020

IEEE Trans. Signal Process.

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We study the problem of detecting and locating change points in high-dimensional Vector Autoregressive (VAR) models, whose transition matrices exhibit low rank plus sparse structure. We first address the problem of detecting a single change point using an exhaustive search algorithm and establish a finite sample error bound for its accuracy. Next, we extend the results to the case of multiple change points that can grow as a function of the sample size. Their detection is based on a twostep algorithm, wherein the first step, an exhaustive search for a candidate change point is employed for overlapping windows, and subsequently a backwards elimination procedure is used to screen out redundant candidates. The two-step strategy yields consistent estimates of the number and the locations of the change points. To reduce computation cost, we also investigate conditions under which a surrogate VAR model with a weakly sparse transition matrix can accurately estimate the change points and their locations for data generated by the original model. This work also addresses and resolves a number of novel technical challenges posed by the nature of the VAR models under consideration. The effectiveness of the proposed algorithms and methodology is illustrated on both synthetic and two real data sets.

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“…Exceptions that do not necessarily focus on linear models include Gorecki et el. (2017), Harvey et al (2016), Bardsley et al (2017), Harris et al (2017), and Wu and Zhou (2018), where nonstationarity is allowed in the observations that covers heteroscedasticity or changing volatilities. Commonly in applications, and as is the case with the data examples studied below, the assumption of heteroscedasticity is much more realistic.…”

Section: Introduction and Main Resultsmentioning

confidence: 99%

Detecting early or late changes in linear models with heteroscedastic errors

Horváth

Miller

Rice

2020

Scandinavian J Statistics

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We construct and study a test to detect possible change points in the regression parameters of a linear model when the model errors and covariates may exhibit heteroscedasticity. Being based on a new trimming scheme for the CUSUM process introduced in Horváth et al. (2020), this test is particularly well suited to detect changes that might occur near the endpoints of the sample. A complete asymptotic theory for the test is developed under the null hypothesis of no change in the regression parameter, and consistency of the test is also established in the presence of a parameter change. Monte Carlo simulations show that our test is comparable to existing methods when the errors are homoscedastic. In contrast, existing methods developed for homoscedastic data are demonstrated to be ill-sized and poorly performing in the presence of heteroscedasticity, while the proposed test continues to perform well in heteroscedastic environments. These results are further demonstrated in a study of the linear connection between the price of crude oil and the U.S. dollar, and in detecting changes points in asset pricing models surrounding the COVID-19 pandemic.

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Change point tests in functional factor models with application to yield curves

Cited by 18 publications

References 36 publications

When does cognitive decline begin? A systematic review of change point studies on accelerated decline in cognitive and neurological outcomes preceding mild cognitive impairment, dementia, and death.

When does cognitive decline begin? A systematic review of change point studies on accelerated decline in cognitive and neurological outcomes preceding mild cognitive impairment, dementia, and death.

Multiple Change Points Detection in Low Rank and Sparse High Dimensional Vector Autoregressive Models

Detecting early or late changes in linear models with heteroscedastic errors

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