Modelling multiple time series via common factors

By Jiazhu Pan, Qiwei Yao. 2008

147 citations recorded by the source corpus.

Subfields: Applied Mathematics, Economics and Econometrics, Finance

How researchers see this paper

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

In Applied Mathematics

1. Clifford Lam
2. Qiwei Yao
3. N. Bathia
4. Jinyuan Chang
5. Bin Guo
6. Lixing Zhu
7. Wang-li Xu
8. Qiang Xia

In Economics and Econometrics

1. Qiwei Yao
2. Clifford Lam
3. Rong Chen
4. Lixing Zhu
5. Wang-li Xu
6. Qiang Xia
7. Jinyuan Chang
8. Xialu Liu

In Finance

1. Clifford Lam
2. Qiwei Yao
3. Rong Chen
4. Victor J. Yohai
5. Xialu Liu
6. Matteo Barigozzi
7. Jinyuan Chang
8. Bin Guo

Citations in the corpus

• Deep Factors for Forecasting (2019, background): “Dynamic factor models (DFMs) have been studied in econo-metrics for decades to model the co-evolvement of multiple time series (Geweke, 1977; Stock & Watson, 2011; Forni et al., 2000; Pan & Yao, 2008).”
• Constrained Factor Models for High-Dimensional Matrix-Variate Time Series (2017, unknown): “…such data either converts the matrix into a vector or modeling the row or column vectors separately (Chamberlain 1983; Chamberlain and Rothschild 1983; Bai 2003; Bai and Ng 2002, 2007; Forni et al. 2000, 2004; Pan and Yao 2008; Lam, Yao, and Bathia 2011; Lam and Yao 2012; Chang, Guo, and Yao 2015).”
• Fitting dynamic factor models to non-stationary time series (2009, background): “Pan and Yao (2008), finally, choose again a different approach on estimating the factor loading space by a stepwise optimisation algorithm on expanding the ”white noise space”: this allows for a generalisation of the traditional factor approach without specifying any (parametric our non-parametric)…”
• Principal component analysis for second-order stationary vector time series (2014, methodology): “It is equal to 0 if and only if M(H1) = M(H2), and to 1 if and only if M(H1) and M(H2) are orthogonal; see, for example, Stewart and Sun (1990) and Pan and Yao (2008).”
• Generalized Dynamic Principal Components (2016, background): “Some generalizations to the nonstationary case are Peña and Poncela (2006) for integrated processes, Pan and Yao (2008) for general nonstationary processes and Motta, Hafner and von Sachs (2011) and Motta and Ombao (2012) for locally stationary processes.”
• Estimation of latent factors for high-dimensional time series (2011, methodology): “Otherwise a discrepancy measure for two linear spaces has to be introduced in order to make statements on the convergence rate of the estimator for the factor loading space M(A); see Pan and Yao (2008).”
• Factor modeling for high-dimensional time series: inference for the number of factors (2012, methodology): “For further details on the implementation of this scheme, we refer to Pan and Yao (2008). Note that once we have obtained an estimator B̂, the columns of  may be taken as any r orthonormal eigenvectors of matrix (Ip − B̂B̂) corresponding to the eigenvalue 1.”
• Improved penalization for determining the number of factors in approximate factor models (2010, background): “Other recent criteria based on the eigenvalues of the covariance matrix are in Yao and Pan (2008) and Onatski (2010).”
• Factor models for matrix-valued high-dimensional time series (2016, background): “In the past decades, factor models for high-dimensional time series data have drawn 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;”
• Consistently determining the number of factors in multivariate volatility modelling (2015, methodology): “We need some regularity conditions similar to those in Lam, Yao, and Bathia (2011), Lam and Yao (2012), and Pan and Yao (2008).”