Identifying the number of factors using a white noise test
DOI10.1016/j.spl.2019.04.011zbMath1427.62099OpenAlexW2944212438WikidataQ127940033 ScholiaQ127940033MaRDI QIDQ2322652
Publication date: 5 September 2019
Published in: Statistics \& Probability Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.spl.2019.04.011
Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Hypothesis testing in multivariate analysis (62H15) Bootstrap, jackknife and other resampling methods (62F40) White noise theory (60H40) Asymptotic properties of parametric tests (62F05)
Cites Work
- Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
- Factor modeling for high-dimensional time series: inference for the number of factors
- Identifying the number of factors from singular values of a large sample auto-covariance matrix
- Basic properties of strong mixing conditions. A survey and some open questions
- Covariance regularization by thresholding
- Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors
- Consistently determining the number of factors in multivariate volatility modelling
- Testing for high-dimensional white noise using maximum cross-correlations
- Determining the Number of Factors in Approximate Factor Models
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