Target PCA: Transfer Learning Large Dimensional Panel Data
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Jianqing Fan & Yuan Liao & Martina Mincheva, 2013.
"Large covariance estimation by thresholding principal orthogonal complements,"
Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(4), pages 603-680, September.
- Fan, Jianqing & Liao, Yuan & Mincheva, Martina, 2011. "Large covariance estimation by thresholding principal orthogonal complements," MPRA Paper 38697, University Library of Munich, Germany.
- Jushan Bai & Serena Ng, 2002.
"Determining the Number of Factors in Approximate Factor Models,"
Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
- Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
- Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
- Qihui Chen, 2022. "A Unified Framework for Estimation of High-dimensional Conditional Factor Models," Papers 2209.00391, arXiv.org.
- Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, May.
- Chow, Gregory C & Lin, An-loh, 1971.
"Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series,"
The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
- Tom Doan, "undated". "CHOWLIN: RATS procedure to distribute a series to a higher frequency using related series," Statistical Software Components RTS00036, Boston College Department of Economics.
- Tom Doan, "undated". "DISAGGREGATE: RATS procedure to implement general disaggregation (interpolation/distribution) procedure," Statistical Software Components RTS00050, Boston College Department of Economics.
- Boivin, Jean & Ng, Serena, 2006.
"Are more data always better for factor analysis?,"
Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
- Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
- Jin, Sainan & Miao, Ke & Su, Liangjun, 2021.
"On factor models with random missing: EM estimation, inference, and cross validation,"
Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
- Su, Liangjun & Miao, Ke & Jin, Sainan, 2019. "On Factor Models with Random Missing: EM Estimation, Inference, and Cross Validation," Economics and Statistics Working Papers 4-2019, Singapore Management University, School of Economics.
- Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
- Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Choi, Sung Hoon & Kim, Donggyu, 2023. "Large volatility matrix analysis using global and national factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1917-1933.
- Zhaoxing Gao & Ruey S. Tsay, 2023. "Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors," Papers 2307.07689, arXiv.org.
- Fan, Jianqing & Xue, Lingzhou & Yao, Jiawei, 2017. "Sufficient forecasting using factor models," Journal of Econometrics, Elsevier, vol. 201(2), pages 292-306.
- Zhaoxing Gao & Ruey S. Tsay, 2021. "Divide-and-Conquer: A Distributed Hierarchical Factor Approach to Modeling Large-Scale Time Series Data," Papers 2103.14626, arXiv.org.
- Yinchu Zhu, 2019. "How well can we learn large factor models without assuming strong factors?," Papers 1910.10382, arXiv.org, revised Nov 2019.
- Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
- Xiong, Ruoxuan & Pelger, Markus, 2023. "Large dimensional latent factor modeling with missing observations and applications to causal inference," Journal of Econometrics, Elsevier, vol. 233(1), pages 271-301.
- He, Yong & Zhang, Mingjuan & Zhang, Xinsheng & Zhou, Wang, 2020. "High-dimensional two-sample mean vectors test and support recovery with factor adjustment," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
- Barigozzi, Matteo & Lippi, Marco & Luciani, Matteo, 2021. "Large-dimensional Dynamic Factor Models: Estimation of Impulse–Response Functions with I(1) cointegrated factors," Journal of Econometrics, Elsevier, vol. 221(2), pages 455-482.
- Matteo Barigozzi, 2023. "Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review," Papers 2303.11777, arXiv.org, revised Dec 2023.
- Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
- Jianqing Fan & Yuan Liao & Han Liu, 2016. "An overview of the estimation of large covariance and precision matrices," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
- Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
- Fan, Jianqing & Liao, Yuan & Shi, Xiaofeng, 2015.
"Risks of large portfolios,"
Journal of Econometrics, Elsevier, vol. 186(2), pages 367-387.
- Jianqing Fan & Yuan Liao & Xiaofeng Shi, 2013. "Risks of Large Portfolios," Papers 1302.0926, arXiv.org.
- Fan, Jianqing & Liao, Yuan & Shi, Xiaofeng, 2013. "Risks of large portfolios," MPRA Paper 44206, University Library of Munich, Germany.
- Yuefeng Han & Rong Chen & Dan Yang & Cun-Hui Zhang, 2020. "Tensor Factor Model Estimation by Iterative Projection," Papers 2006.02611, arXiv.org, revised May 2022.
- Jushan Bai & Serena Ng, 2020. "Simpler Proofs for Approximate Factor Models of Large Dimensions," Papers 2008.00254, arXiv.org.
- Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany.
- Zhang, Yixiao & Yu, Cindy L. & Li, Haitao, 2022. "Nowcasting GDP Using Dynamic Factor Model with Unknown Number of Factors and Stochastic Volatility: A Bayesian Approach," Econometrics and Statistics, Elsevier, vol. 24(C), pages 75-93.
- Barigozzi, Matteo & Trapani, Lorenzo, 2020.
"Sequential testing for structural stability in approximate factor models,"
Stochastic Processes and their Applications, Elsevier, vol. 130(8), pages 5149-5187.
- Matteo Barigozzi & Lorenzo Trapani, 2017. "Sequential testing for structural stability in approximate factor models," Papers 1708.02786, arXiv.org, revised Mar 2020.
- Matteo Barigozzi & Lorenzo Trapani, 2018. "Sequential testing for structural stability in approximate factor models," Discussion Papers 18/04, University of Nottingham, Granger Centre for Time Series Econometrics.
- Yunus Emre Ergemen & Carlos Vladimir RodrÃguez-Caballero, 2016. "A Dynamic Multi-Level Factor Model with Long-Range Dependence," CREATES Research Papers 2016-23, Department of Economics and Business Economics, Aarhus University.
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2023-09-25 (Econometrics)
- NEP-ETS-2023-09-25 (Econometric Time Series)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2308.15627. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.