Quasi-maximum likelihood estimation of break point in high-dimensional factor models

Duan, Jiangtao; Bai, Jushan; Han, Xu

doi:10.1016/j.jeconom.2021.12.011

Cited by 14 publications

(3 citation statements)

References 18 publications

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“…The presence of structural breaks or change-points represents another important cause of deviation from the assumption of stationarity. Many papers have investigated this problem from an off-line perspective in the static factor model approach, proposing various tests for the presence of breaks and, possibly, estimators for their location: see Breitung andEickmeier (2011), Chen et al (2014), Han and Inoue (2014), Corradi and Swanson (2014), Yamamoto and Tanaka (2015), Cheng et al (2016), Baltagi et al (2017), Bai et al (2017), Ma and Su (2018), Duan et al (2023). The case of the restricted dynamic factor model has been studied by Barigozzi et al (2018a) who not only allow for changes in the loadings, but also in the number of factors, and in the autocorrelation function of the factors, as well as change-points in the idiosyncratic second-order structure.…”

Section: Factors and Breaksmentioning

confidence: 99%

Inferential theory for generalized dynamic factor models

Barigozzi

Hallin

Luciani

et al. 2024

Journal of Econometrics

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Section: Factors and Breaksmentioning

confidence: 99%

Inferential theory for generalized dynamic factor models

Barigozzi

Hallin

Luciani

et al. 2024

Journal of Econometrics

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“…k may be the same as 0 k+1 (when cases (i) and (iii) do not occur at the break time). It is worthwhile to point out that case (i) is similar to the approximate factor model with structural breaks; see, for example, Breitung andEickmeier (2011), Chen, Dolado, andGonzalo (2014), Han and Inoue (2015), Cheng, Liao, and Schorfheide (2016), Baltagi, Kao, and Wang (2017), Shi (2020), andDuan, Bai, andHan (2022). However, the aforementioned papers consider the case of a single structural break in the factor loadings and usually assume that the covariance of the idiosyncratic error components is time invariant.…”

Section: Model Structure For Multiple Structural Breaksmentioning

confidence: 99%

Detection of Multiple Structural Breaks in Large Covariance Matrices

Yu-ning

Fryźlewicz

2022

Journal of Business & Economic Statistics

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This article studies multiple structural breaks in large contemporaneous covariance matrices of highdimensional time series satisfying an approximate factor model. The breaks in the second-order moment structure of the common components are due to sudden changes in either factor loadings or covariance of latent factors, requiring appropriate transformation of the factor models to facilitate estimation of the (transformed) common factors and factor loadings via the classical principal component analysis. With the estimated factors and idiosyncratic errors, an easy-to-implement CUSUM-based detection technique is introduced to consistently estimate the location and number of breaks and correctly identify whether they originate in the common or idiosyncratic error components. The algorithms of Wild Binary Segmentation for Covariance (WBS-Cov) and Wild Sparsified Binary Segmentation for Covariance (WSBS-Cov) are used to estimate breaks in the common and idiosyncratic error components, respectively. Under some technical conditions, the asymptotic properties of the proposed methodology are derived with near-optimal rates (up to a logarithmic factor) achieved for the estimated breaks. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the developed method and its comparison with other existing approaches. We finally apply our method to study the contemporaneous covariance structure of daily returns of S&P 500 constituents and identify a few breaks including those occurring during the 2007-2008 financial crisis and the recent coronavirus (COVID-19) outbreak. An R package "BSCOV" is provided to implement the proposed algorithms. Supplementary materials for this article are available online.

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“…Common factors are modeled by principal components in macroeconomic time series. Knowing their number is key for forecasting (Stock and Watson 2002), break point estimation (Duan, Bai, and Han 2023), or in general for asset pricing models, where the number of common factors is used to verify whether all systematic determinants in the crosssection of returns are accounted for. Models with common factors are popular in panel data (Sul 2019).…”

Section: Introductionmentioning

confidence: 99%

xtnumfac: A battery of estimators for the number of common factors in time series and panel-data models

Ditzen

Reese

2023

The Stata Journal: Promoting communications on statistics and S

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In this article, we introduce a new community-contributed command, xtnumfac, for estimating the number of common factors in time-series and panel datasets using the methods of Bai and Ng (2002, Econometrica 70: 191–221), Ahn and Horenstein (2013, Econometrica 81: 1203–1227), Onatski (2010, Review of Economics and Statistics 92: 1004–1016), and Gagliardini, Ossola, and Scaillet (2019, Journal of Econometrics 212: 503–521). Common factors are usually unobserved or unobservable. In time series, they influence all predictors, while in paneldata models, they influence all cross-sectional units at different degrees. Examples are shocks from oil prices, inflation, or demand or supply shocks. Knowledge about the number of factors is key for multiple econometric estimation methods, such as Pesaran (2006, Econometrica 74: 967–1012), Bai (2009, Econometrica 77: 1229–1279), Norkute et al. (2021, Journal of Econometrics 220: 416–446), and Kripfganz and Sarafidis (2021, Stata Journal 21: 659–686). This article discusses a total of 10 methods to estimate the number of common factors. Examples based on Kapetanios, Pesaran, and Reese (2021, Journal of Econometrics 221: 510–541) show that U.S. house prices are exposed to up to 10 common factors. Therefore, when one fits models with house prices as a dependent variable, the number of factors must be considered.

show abstract

Quasi-maximum likelihood estimation of break point in high-dimensional factor models

Cited by 14 publications

References 18 publications

Inferential theory for generalized dynamic factor models

Inferential theory for generalized dynamic factor models

Detection of Multiple Structural Breaks in Large Covariance Matrices

xtnumfac: A battery of estimators for the number of common factors in time series and panel-data models

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