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Articles

State-Varying Factor Models of Large Dimensions

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Xin-Bing Kong, Jin-Guan Lin, Cheng Liu & Guang-Ying Liu. (2023) Discrepancy Between Global and Local Principal Component Analysis on Large-Panel High-Frequency Data. Journal of the American Statistical Association 118:542, pages 1333-1344.
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Markus Pelger & Ruoxuan Xiong. (2022) Interpretable Sparse Proximate Factors for Large Dimensions. Journal of Business & Economic Statistics 40:4, pages 1642-1664.
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Articles from other publishers (5)

Sung Hoon Choi. (2023) Feasible weighted projected principal component analysis for semi-parametric factor models. The Econometrics Journal 26:2, pages 215-234.
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Luyang ChenMarkus Pelger & Jason Zhu. (2023) Deep Learning in Asset Pricing. Management Science.
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Gaetan Bakalli, Stéphane Guerrier & Olivier Scaillet. (2023) A penalized two-pass regression to predict stock returns with time-varying risk premia. Journal of Econometrics, pages 105375.
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Liyuan Cui, Yongmiao Hong, Yingxing Li & Junhui Wang. (2019) A Machine Learning Approach to Estimating Large Positive Definite Covariance Matrix of High Frequency Data. SSRN Electronic Journal.
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Markus Pelger & Ruoxuan Xiong. (2018) Interpretable Proximate Factors for Large Dimensions. SSRN Electronic Journal.
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