Arbeitspapier
Inference of Break-Points in High-Dimensional Time Series
We consider a new procedure for detecting structural breaks in mean for high- dimensional time series. We target breaks happening at unknown time points and locations. In particular, at a fixed time point our method is concerned with either the biggest break in one location or aggregating simultaneous breaks over multiple locations. We allow for both big or small sized breaks, so that we can 1), stamp the dates and the locations of the breaks, 2), estimate the break sizes and 3), make inference on the break sizes as well as the break dates. Our theoretical setup incorporates both temporal and crosssectional dependence, and is suitable for heavy-tailed innovations. We derive the asymptotic distribution for the sizes of the breaks by extending the existing powerful theory on local linear kernel estimation and high dimensional Gaussian approximation to allow for trend stationary time series with jumps. A robust long-run covariance matrix estimation is proposed, which can be of independent interest. An application on detecting structural changes of the US unemployment rate is considered to illustrate the usefulness of our method.
- Sprache
-
Englisch
- Erschienen in
-
Series: IRTG 1792 Discussion Paper ; No. 2019-013
- Klassifikation
-
Wirtschaft
Mathematical and Quantitative Methods: General
- Thema
-
high-dimensional time series
multiple change-points
Gaussian approximation
nonparametric estimation
heavy tailed
long-run covariance matrix
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Chen, Likai
Wang, Weining
Wu, Wei Biao
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
- (wo)
-
Berlin
- (wann)
-
2019
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Chen, Likai
- Wang, Weining
- Wu, Wei Biao
- Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
Entstanden
- 2019
Ähnliche Objekte (12)
Inference for high-dimensional sparse econometric models
Inference of breakpoints in high-dimensional time series
Uniform inference in high-dimensional gaussian graphical models
Statistical inference on high-dimensional tail event curves
Program evaluation and causal inference with high-dimensional data
Advances in Machine Learning: Valid Inference about High-Dimensional Parameters
Robust inference in high-dimensional approximately sparse quantile regression models
Inference on treatment effects after selection amongst high-dimensional controls
Inference on treatment effects after selection amongst high-dimensional controls
Inference for multivariate and high-dimensional data in heterogeneous designs
Bayesian Inference for High-Dimensional Data with Applications to Portfolio Theory
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
Inference for high-dimensional sparse econometric models
Inference of breakpoints in high-dimensional time series
Uniform inference in high-dimensional gaussian graphical models
Statistical inference on high-dimensional tail event curves
Program evaluation and causal inference with high-dimensional data
Advances in Machine Learning: Valid Inference about High-Dimensional Parameters
Robust inference in high-dimensional approximately sparse quantile regression models
Inference on treatment effects after selection amongst high-dimensional controls
Inference on treatment effects after selection amongst high-dimensional controls
Inference for multivariate and high-dimensional data in heterogeneous designs
Bayesian Inference for High-Dimensional Data with Applications to Portfolio Theory
Valid post-selection inference in high-dimensional approximately sparse quantile regression models
Inference for high-dimensional sparse econometric models
Inference of breakpoints in high-dimensional time series
Uniform inference in high-dimensional gaussian graphical models
Statistical inference on high-dimensional tail event curves
Program evaluation and causal inference with high-dimensional data
Advances in Machine Learning: Valid Inference about High-Dimensional Parameters
Robust inference in high-dimensional approximately sparse quantile regression models
Inference on treatment effects after selection amongst high-dimensional controls
Inference on treatment effects after selection amongst high-dimensional controls
Inference for multivariate and high-dimensional data in heterogeneous designs
Bayesian Inference for High-Dimensional Data with Applications to Portfolio Theory