Least‐squares Estimation of an Unknown Number of Shifts in a Time Series

By Marc Lavielle, Eric Moulines. 2000

308 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a482988703f2c5a96fa2b6e2dfe23fc0462cd70f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

In Statistics and Probability

Citations in the corpus

Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns (2004, background): “Lavielle and Moulines (2000) show that, to get a consistent number of breaks in the least squares estimation, typically the penalization for the time series with 0 d 1/2 should be higher than the one of Eq.”
How far do shocks move across borders? Examining volatility transmission in major agricultural futures markets (2014, methodology): “To divide our working sample into a period pre-crisis and a period post-crisis, we apply the test for the presence of structural breaks proposed by Lavielle and Moulines (2000).”
Market interdependence and volatility transmission among major crops (2016, methodology): “…functions on conditional volatility 14 4.2 Dynamic conditional correlations 18 A.1 Structural break tests on squared returns based on Lavielle and Moulines (2000) 31 A.2 Impulse-response functions on conditional volatility, subsample periods 32 A.3 Impulse-response functions on…”
Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model (2004, methodology): “6 Using the methodology of Lavielle and Moulines (2000) .”
Identification of the Hurst Index of a Step Fractional Brownian Motion (2000, background): “[ 13 ]) to model the profile of a mountain with a FBM and in this example the Hurst parameter is given by the nature of the subsoil.”
Detecting Changes in Slope With an L0 Penalty (2017, methodology): “A standard approach to detecting changes in mean is to attempt to find the “best” piecewise constant mean function, where best is defined based on its fit to the data penalized by a measure of complexity of the mean function (Yao 1988; Lavielle and Moulines 2000).”
Multiple Change-Point Estimation With a Total Variation Penalty (2010, methodology): “This problem has recently received much attention on the theoretical side, both in a nonasymptotic and in an asymptotic setting by Massart (2005) and Yao and Au (1989), Lavielle and Moulines (2000), Boysen et al. (2009), respectively.”
Minimal Penalties for Gaussian Model Selection (2007, background): “The problem of detecting the change points in a piecewise constant signal has already been considered by Yao [41] and more recently by Lavielle and Moulines [25] but their point of view was quite different since it was asymptotic and they assumed a fixed number of change points.”
Selective review of offline change point detection methods (2020, methodology): “Historically, the Gaussian distribution was first used, to model mean-shifts [52–54] and scale shifts [39, 50, 55].”
Dealing with Structural Breaks (2005, background): “Note that the latter is also imposed in Lavielle and Moulines (2000), though they note that it can be relaxed with stronger conditions on z t u t or by constraining the estimates to lie in a compact set.”
Univariate mean change point detection: Penalization, CUSUM and optimality (2018, result): “Lavielle and Moulines (2000) studied the ℓ 0 -penalization approach under general distributions, and showed that if one chooses the penalization parameter λ properly, then one would get similar asymptotic results to the case where the model assumes Gaussian noise.”
Wild binary segmentation for multiple change-point detection (2014, background): “For an unknown but bounded N , Lavielle and Moulines (2000) consider penalised least-squares estimation, with a penalty linear in the number of change-points, and show its consistency for the number and locations of change-points for dependent ε t ’s, including the cases of strong mixing and…”
Multiple‐change‐point detection for high dimensional time series via sparsified binary segmentation (2015, background): “(Ombao et al. 2005). Vert and Bleakley (2010) describe other interesting examples of multivariate, nonstationary time series in many other fields, such as signal processing, biology and medicine. As arguably one of the simplest forms of departure from stationarity, we consider a class of piecewise s”
Detecting multiple breaks in financial market volatility dynamics (2002, methodology): “We focus on the Kokoszka and Leipus (2000) and Lavielle and Moulines (2000) tests which monitor nonlinear transformations of returns processes (in square and absolute returns) without the need to specify any particular, restrictive functional form of the process.”
A Test Against Spurious Long Memory (2011, methodology): “First, apply the method of Lavielle and Moulines (2000) to estimate the number and locations of the level shifts.”
A review of change point detection methods (2018, methodology): “Historically, the Gaussian distribution was often used to model mean and scale shifts [5, 82, 85, 91, 123].”