Common Breaks in Means for Cross‐Correlated Fixed‐<i>T</i> Panel Data

Westerlund, Joakim

doi:10.1111/jtsa.12407

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…It is, however, still the same breaking constant-only model that is being considered, and many models of interest involve more general regressors. Antoch et al (2019), Baltagi et al (2016), Boldea et al (2020), Hidalgo and Schafgans (2017), and Li et al (2016) do for a general linear panel data regression model what Horváth and Hušková (2012), Kim (2014), and Westerlund (2019) do for the constant-only model. In particular, while Antoch et al (2019), and Hidalgo and Schafgans (2017) propose tests for the presence of a structural break, Baltagi et al (2016), Boldea et al (2020), and Li et al (2016) take the existence of a break as given and focus instead on the breakpoint estimation problem.…”

Section: Introductionmentioning

confidence: 80%

“…Such heterogeneity is important in general, and it is particularly relevant in the type of noisy panels that we have in mind where typically the regressors explain only a small fraction of the variation in the dependent variable (see Capelle-Blanchard and Desroziers, 2020, in the context of COVID-19 and stock returns). This consideration motivated Horváth and Hušková (2012), Kim (2014), and Westerlund (2019) to extend the work of Bai (2010) to the case when the stochastic component of the data admits to a common factor, or "interactive effects", representation. 1 Such representations have been shown to be very effective at capturing unobserved heterogeneity, and they are therefore very popular.…”

Section: Introductionmentioning

confidence: 99%

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Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19

Karavias¹,

Narayan²,

Westerlund³

2021

Preprint

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Dealing with structural breaks is an important step in most, if not all, empirical economic research. This is particularly true in panel data comprised of many cross-sectional units, such as individuals, firms or countries, which are all affected by major events. The COVID-19 pandemic has affected most sectors of the global economy, and there is by now plenty of evidence to support this. The impact on stock markets is, however, still unclear. The fact that most markets seem to have partly recovered while the pandemic is still ongoing suggests that the relationship between stock returns and COVID-19 has been subject to structural change. It is therefore important to know if a structural break has occurred and, if it has, to infer the date of the break. In the present paper we take this last observation as a source of motivation to develop a new break detection toolbox that is applicable to different sized panels, easy to implement and robust to general forms of unobserved heterogeneity. The toolbox, which is the first of its kind, includes a test for structural change, a break date estimator, and a break date confidence interval. Application to a panel covering 61 countries from January 3 to September 25, 2020, leads to the detection of a structural break that is dated to the first week of April. The effect of COVID-19 is negative before the break and zero thereafter, implying that while markets did react, the reaction was short-lived. A possible explanation for this is the quantitative easing programs announced by central banks all over the world in the second half of March.

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Section: Introductionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19

Karavias¹,

Narayan²,

Westerlund³

2021

Preprint

Self Cite

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“…For the general changepoint problem of a sparse or dense change in the mean, the literature is mostly concentrated on methods that either allow for sparse changes but assume cross-independence (Xie and Siegmund, 2013;Jirak, 2015;Cho and Fryzlewicz, 2015;Cho, 2016;Bardwell et al, 2019), or allow cross-dependence but assume changes are dense (Horváth and Hušková, 2012;Li et al, 2019;Bhattacharjee et al, 2019;Westerlund, 2019). The inspect method of Wang and Samworth (2018) is a notable exception from this rule as it is designed to estimate sparse changes in the mean of potentially cross-correlated data.…”

Section: Introductionmentioning

confidence: 99%

Scalable changepoint and anomaly detection in cross-correlated data with an application to condition monitoring

Tveten¹,

Eckley²,

Fearnhead³

2020

Preprint

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Motivated by a condition monitoring application arising from subsea engineering we derive a novel, scalable approach to detecting anomalous mean structure in a subset of correlated multivariate time series. Given the need to analyse such series efficiently we explore a computationally efficient approximation of the maximum likelihood solution to the resulting modelling framework, and develop a new dynamic programming algorithm for solving the resulting Binary Quadratic Programme when the precision matrix of the time series at any given time-point is banded. Through a comprehensive simulation study, we show that the resulting methods perform favourably compared to competing methods both in the anomaly and change detection settings, even when the sparsity structure of the precision matrix estimate is misspecified. We also demonstrate its ability to correctly detect faulty time-periods of a pump within the motivating application.

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“…Cho and Fryzlewicz [2015] segmented the second-order structure of a high-dimensional time series and used the CUSUM statistic to detect multiple change points. Further, Kim [2014], Baltagi et al [2016], Barigozzi et al [2018], Westerlund [2018] investigated estimation of the change point in panel data, wherein the cross-sectional dependence is modeled by a common factor model, which effectively makes the cross-sectional dependence low-dimensional.…”

Section: Introductionmentioning

confidence: 99%

Change Point Estimation in Panel Data with Temporal and Cross-sectional Dependence

Bhattacharjee,

Banerjee,

Michailidis

2019

Preprint

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We study the problem of detecting a common change point in large panel data based on a mean shift model, wherein the errors exhibit both temporal and cross-sectional dependence.A least squares based procedure is used to estimate the location of the change point. Further, we establish the convergence rate and obtain the asymptotic distribution of the least squares estimator. The form of the distribution is determined by the behavior of the norm difference of the means before and after the change point. Since the behavior of this norm difference is, a priori, unknown to the practitioner, we also develop a novel data driven adaptive procedure that provides valid confidence intervals for the common change point, without requiring any such knowledge. Numerical work based on synthetic data illustrates the performance of the estimator in finite samples under different settings of temporal and cross-sectional dependence, sample size and number of panels. Finally, we examine an application to financial stock data and discuss the identified change points.

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Common Breaks in Means for Cross‐Correlated Fixed‐T Panel Data

Cited by 8 publications

References 17 publications

Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19

Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19

Scalable changepoint and anomaly detection in cross-correlated data with an application to condition monitoring

Change Point Estimation in Panel Data with Temporal and Cross-sectional Dependence

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