Estimation of latent factors for high-dimensional time series

By Clifford Lam, Qiwei Yao, N. Bathia. 2011

228 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/75c1b2c076a25601bd64eaa5482c1fee3e4cff33

How researchers see this paper

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

In Economics and Econometrics

1. Clifford Lam
2. Rong Chen
3. Xialu Liu
4. Qiwei Yao
5. Dong Wang
6. S. Ling
7. Jinyuan Chang

In Finance

1. Clifford Lam
2. Rong Chen
3. Xialu Liu
4. Dong Wang
5. Qiwei Yao
6. S. Ling
7. Christian Francq
8. Jean-Michel Zakoian

In Statistics and Probability

1. Rong Chen
2. Qiwei Yao
3. Clifford Lam
4. Xialu Liu
5. Dong Wang
6. Jianqing Fan
7. Yuan Liao
8. Elynn Y. Chen

Citations in the corpus

• Nonparametric eigenvalue-regularized precision or covariance matrix estimator (2016, background): “In Lam et al. (2011), they consider {xi} being a stationary time series and {ǫi} being a white noise.”
• Factor models for matrix-valued high-dimensional time series (2016, methodology): “…great attention from both econometricians and statisticians (e.g. Chamberlain and Rothschild, 1983; Forni et al., 2000; Bai and Ng, 2002; Hallin and Liška, 2007; Pan and Yao, 2008; Lam et al., 2011; Fan et al., 2011; Lam and Yao, 2012; Fan et al., 2013; Chang et al., 2015; Liu and Chen, 2016).”
• Constrained Factor Models for High-Dimensional Matrix-Variate Time Series (2017, unknown): “…factor analysis of such data either converts the matrix into a vector or modeling the row or column vectors separately (Chamberlain 1983, Chamberlain & Rothschild 1983, Bai 2003, Bai & Ng 2002, 2007, Forni et al. 2000, 2004, Pan & Yao 2008, Lam et al. 2011, Lam & Yao 2012, Chang et al. 2015).”
• High‐dimensional covariance matrix estimation (2019, background): “In view of this, Lam et al. (2011) proposed a \statistical" factor model, where Af i is viewed as signal and ϵ i is viewed as noise, with the key assumption that f ϵ i g is a vector white noise.”
• Convolutional autoregressive models for functional time series (2016, unknown): “Many empirical results show that the implied volatility curves are unit-root-nonstationary (Fengler et al., 2005; Goncalves and Guidolin, 2006; Lam et al., 2011).”
• Principal component analysis for second-order stationary vector time series (2014, unknown): “nt multiple time series in terms of a few factors defined in various ways; see, among others, Pen˜a and Box (1987), Stock and Watson (2005), Bai and Ng (2002), Forni et al. (2005), Pan and Yao (2008), Lam et al. (2011), Lam and Yao (2012) and Chang et al. (2013a). A new dimension reduction method is ”
• Factor modeling for high-dimensional time series: inference for the number of factors (2012, background): “Then following the proof of Theorem 1 of [13], we have the following for k = 1, .”