Nonstationary dynamic factor analysis

By D. Pena, Pilar Poncela. 2006

151 citations recorded by the source corpus.

Subfields: Applied Mathematics, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/95fbe3f660ea805658d012ffdad9d956452999e3

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. Lixing Zhu
5. Wang-li Xu
6. Qiang Xia
7. Jiazhu Pan
8. D. Pena

In Economics and Econometrics

1. Clifford Lam
2. Qiwei Yao
3. Matteo Barigozzi
4. Jiazhu Pan
5. Victor J. Yohai
6. N. Bathia
7. D. Pena
8. Matteo Luciani

In Finance

1. Clifford Lam
2. Qiwei Yao
3. Jiazhu Pan
4. Victor J. Yohai
5. N. Bathia
6. Lixing Zhu
7. Wang-li Xu
8. Qiang Xia

Citations in the corpus

• Factor modeling for high-dimensional time series: inference for the number of factors (2012, methodology): “1) may be traced back at least to [18]; see also its further development in dealing with cointegrated factors in [19].”
• Spatial dynamic factor analysis (2008, background): “Remaining temporal dependence on the idiosyncratic errors can also be considered (Lopes and Carvalho 2007 and Peña and Poncela 2006) but this was not pursued here.”
• Estimation of latent factors for high-dimensional time series (2011, methodology): “This weak convergence has been established when, for example, {xt} is an integrated process of order 2 by Peña and Poncela (2006). It can also be proved for other processes with linear trends, random walk or long memories.”
• Modelling multiple time series via common factors (2008, methodology): “The latter was investigated in the context of factor models by, for example, Ahn (1997), and Peña & Poncela (2006).”
• 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.”
• Consistently determining the number of factors in multivariate volatility modelling (2015, background): “Relevant efforts look to building a factor model for nonstationary multivariate time series, for example, Peña and Poncela (2006) and Pan and Yao (2008).”
• Non-Stationary Dynamic Factor Models for Large Datasets (2016, background): “As regards (II), with the exception of Bai and Ng (2004), DFMs for I(1) variables are studied under the assumption of stationary idiosyncratic components, see e.g. Bai (2004) and Peña and Poncela (2006) (which is however a model with fixed n).”