797
Views
5
CrossRef citations to date
0
Altmetric
Articles

Interpretable Sparse Proximate Factors for Large Dimensions

&

References

  • Aït-Sahalia, Y., and D. Xiu (2019), “Principal Component Analysis of High-Frequency Data,” Journal of the American Statistical Association, 114, 287–303. DOI: 10.1080/01621459.2017.1401542.
  • Ancona-Navarrete, M. A., and Tawn, J. A. (2000), “A Comparison of Methods for Estimating the Extremal Index,” Extremes, 3, 5–38.
  • Anderson, T. W. (1962), “An Introduction to Multivariate Statistical Analysis,” Discussion paper, New York: Wiley.
  • Andreou, E., Gagliardini, P., Ghysels, E., and Rubin, M. (2019), “Inference in Group Factor Models With an Application to Mixed-Frequency Data,” Econometrica, 87, 1267–1305. DOI: 10.3982/ECTA14690.
  • Bai, J. (2003), “Inferential Theory for Factor Models of Large Dimensions,” Econometrica, 71, 135–171. DOI: 10.1111/1468-0262.00392.
  • Bai, J., and Liao, Y. (2016), “Efficient Estimation of Approximate Factor Models Via Penalized Maximum Likelihood,” Journal of Econometrics, 191, 1–18. DOI: 10.1016/j.jeconom.2015.10.003.
  • Bai, J., and Ng, S. (2002), “Determining the Number of Factors in Approximate Factor Models,” Econometrica, 70, 191–221. DOI: 10.1111/1468-0262.00273.
  • Bai, J., and Ng, S. (2006), “Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions,” Econometrica, 74, 1133–1150.
  • Bai, J., and Ng, S. (2008), “Forecasting Economic Time Series Using Targeted Predictors,” Journal of Econometrics, 146, 304–317. DOI: 10.1016/j.jeconom.2008.08.010.
  • Bai, J., and Ng, S. (2019), “Rank Regularized Estimation of Approximate Factor Models,” Journal of Econometrics, 212, 78–96.
  • Bhattacharya, A., and Dunson, D. B. (2011), “Sparse Bayesian Infinite Factor Models,” Biometrika, 98, 291–306. DOI: 10.1093/biomet/asr013.
  • Boivin, J., and Ng, S. (2005), “Understanding and Comparing Factor-Based Forecasts,” Discussion paper, National Bureau of Economic Research.
  • Boivin, J., and Ng, S. (2006), “Are More Data Always Better for Factor Analysis?” Journal of Econometrics, 132, 169–194.
  • Candès, E. J., Li, X., Ma, Y., and Wright, J. (2011), “Robust Principal Component Analysis?,” Journal of the ACM (JACM), 58, 11. DOI: 10.1145/1970392.1970395.
  • Candès, E. J., and Tao, T. (2010), “The Power of Convex Relaxation: Near-Optimal Matrix Completion,” IEEE Transactions on Information Theory, 56, 2053–2080. DOI: 10.1109/TIT.2010.2044061.
  • Choi, J., Oehlert, G., and Zou, H. (2010), “A Penalized Maximum Likelihood Approach to Sparse Factor Analysis,” Statistics and its Interface, 3, 429–436. DOI: 10.4310/SII.2010.v3.n4.a1.
  • Chudik, A., Pesaran, M. H., and Tosetti, E. (2011), “Weak and Strong Cross-Section Dependence and Estimation of Large Panels,” 14, C45–C90.
  • Coles, S., Bawa, J., Trenner, L., and Dorazio, P. (2001), An Introduction to Statistical Modeling of Extreme Values (Vol. 208), London: Springer.
  • Connor, G., Hagmann, M., and Linton, O. (2012), “Efficient Semiparametric Estimation of the Fama–French Model and Extensions,” Econometrica, 80, 713–754.
  • Connor, G., and Linton, O. (2007), “Semiparametric Estimation of a Characteristic-Based Factor Model of Common Stock Returns,” Journal of Empirical Finance, 14, 694–717. DOI: 10.1016/j.jempfin.2006.10.001.
  • Diebold, F. X., and Li, C. (2006), “Forecasting the Term Structure of Government Bond Yields,” Journal of Econometrics, 130, 337–364. DOI: 10.1016/j.jeconom.2005.03.005.
  • Fama, E. F., and French, K. R. (1992), “The Cross-Section of Expected Stock Returns,” The Journal of Finance, 47, 427–465. DOI: 10.1111/j.1540-6261.1992.tb04398.x.
  • Fan, J., Ke, Y., and Liao, Y. (2016), “Robust Factor Models with Explanatory Proxies,” arXiv:1603.07041.
  • Fan, J., Liao, Y., and Mincheva, M. (2013), “Large Covariance Estimation by Thresholding Principal Orthogonal Complements,” Journal of the Royal Statistical Society, Series B, 75, 603–680. DOI: 10.1111/rssb.12016.
  • Fan, J., Liao, Y., and Wang, W. (2016), “Projected Principal Component Analysis in Factor Models,” Annals of Statistics, 44, 219–254.
  • Hsing, T. (1988), “On the Extreme Order Statistics for a Stationary Sequence,” Advances in Applied Probability, 20, 11–11. DOI: 10.1017/S0001867800017870.
  • Jolliffe, I. T., Trendafilov, N. T., and Uddin, M. (2003), “A Modified Principal Component Technique Based on the LASSO,” Journal of Computational and Graphical Statistics, 12, 531–547. DOI: 10.1198/1061860032148.
  • Jones, C. S. (2001), “Extracting Factors From Heteroskedastic Asset Returns,” Journal of Financial Economics, 62, 293–325. DOI: 10.1016/S0304-405X(01)00079-4.
  • Kaiser, H. F. (1958), “The Varimax Criterion for Analytic Rotation in Factor Analysis,” Psychometrika, 23, 187–200. DOI: 10.1007/BF02289233.
  • Kaufmann, S., and Schumacher, C. (2019), “Bayesian Estimation of Sparse Dynamic Factor Models With Order-Independent and Ex-Post Mode Identification,” Journal of Econometrics, 210, 116–134. DOI: 10.1016/j.jeconom.2018.11.008.
  • Kawano, S., Fujisawa, H., Takada, T., and Shiroishi, T. (2015), “Sparse Principal Component Regression With Adaptive Loading,” Computational Statistics & Data Analysis, 89, 192–203.
  • Kelly, B. T., Pruitt, S., and Su,Y. (2019), “Characteristics Are Covariances: A Unified Model of Risk and Return,” Journal of Financial Economics, 134, 501–524. DOI: 10.1016/j.jfineco.2019.05.001.
  • Kozak, S., Nagel, S., and Santosh, S. (2020): “Shrinking the Cross-Section,” Journal of Financial Economics, 135(2), 271–292. DOI: 10.1016/j.jfineco.2019.06.008.
  • Lan, A. S., Waters, A. E., Studer, C., and Baraniuk, R. G. (2014), “Sparse Factor Analysis for Learning and Content Analytics,” The Journal of Machine Learning Research, 15, 1959–2008.
  • Leadbetter, M. R. (1982), “Extremes and Local Dependence in Stationary Sequences,” Working paper.
  • Lettau, M., and Pelger, M. (2020a), “Estimating Latent Asset Pricing Factors,” Journal of Econometrics, 218, 1–31. DOI: 10.1016/j.jeconom.2019.08.012.
  • Lettau, M., and Pelger, M. (2020b), “Factors That Fit the Time Series and Cross-Section of Stock Returns,” The Review of Financial Studies, 33, 2274–2325.
  • Lucas, J., Carvalho, C., Wang, Q., Bild, A., Nevins, J. R., and West, M. (2006), “Sparse Statistical Modelling in Gene Expression Genomics,” Bayesian Inference for Gene Expression and Proteomics, 1, 1.
  • Ludvigson, S. C., and Ng, S. (2007), “The Empirical Risk Return Relation: A Factor Analysis Approach,” Journal of Financial Economics, 83, 171–222. DOI: 10.1016/j.jfineco.2005.12.002.
  • Ludvigson, S. C., and Ng, S. (2009), “Macro Factors in Bond Risk Premia,” Review of Financial Studies, 22, 5027–5067.
  • Mairal, J., Bach, F., Ponce, J., and Sapiro, G. (2010), “Online Learning for Matrix Factorization and Sparse Coding,” Journal of Machine Learning Research, 11, 19–60.
  • Massacci, D. (2017), “Least Squares Estimation of Large Dimensional Threshold Factor Models,” Journal of Econometrics, 197(1), 101–129. DOI: 10.1016/j.jeconom.2016.11.001.
  • Massacci, D. (2020): “Testing for Regime Changes in Portfolios with a Large Number of Assets: A Robust Approach to Factor Heteroskedasticity,” Journal of Financial Econometrics.
  • McCracken, M. W., and Ng, S. (2016), “FRED-MD: A Monthly Database for Macroeconomic Research,” Journal of Business & Economic Statistics, 34, 574–589.
  • Pati, D., Bhattacharya, N., Pillai, S., and Dunson, D. (2014), “Posterior Contraction in Sparse Bayesian Factor Models for Massive Covariance Matrices,” The Annals of Statistics, 42, 1102–1130. DOI: 10.1214/14-AOS1215.
  • Pelger, M. (2019), “Large-Dimensional Factor Modeling Based on High-Frequency Observations,” Journal of Econometrics, 208, 23–42. DOI: 10.1016/j.jeconom.2018.09.004.
  • Pelger, M. (2020), “Understanding Systematic Risk: A High-Frequency Approach,” Journal of Finance, 75, 2179–2220.
  • Pelger, M., and Xiong, R. (2021), “State-Varying Factor Models of Large Dimensions,” Journal of Business & Economic Statistics.
  • Sigg, C. D., and Buhmann, J. M. (2008), “Expectation–Maximization for Sparse and Non-Negative PCA,” in Proceedings of the 25th International Conference on Machine Learning, pp. 960–967.
  • Stock, J. H., and Watson, M. W. (2002a), “Forecasting Using Principal Components From a Large Number of Predictors,” Journal of the American Statistical Association, 97, 1167–1179. DOI: 10.1198/016214502388618960.
  • Stock, J. H., and Watson, M. W. (2002b), “Macroeconomic Forecasting Using Diffusion Indexes,” Journal of Business & Economic Statistics, 20, 147–162.
  • Tibshirani, R. (1996), “Regression Shrinkage and Selection Via the Lasso,” Journal of the Royal Statistical Society, Series B, pp. 267–288. DOI: 10.1111/j.2517-6161.1996.tb02080.x.
  • Vershynin, R. (2010), “Introduction to the Non-Asymptotic Analysis of Random Matrices,” arXiv: 1011.3027.
  • Xiong, R., and Pelger, M. (2020), “Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference,” Working paper.
  • Zou, H., and Hastie, T. (2005), “Regularization and Variable Selection Via the Elastic Net,” Journal of the Royal Statistical Society, Series B, 67, 301–320. DOI: 10.1111/j.1467-9868.2005.00503.x.
  • Zou, H., Hastie, T., and Tibshirani, R. (2006), “Sparse Principal Component Analysis,” Journal of Computational and Graphical Statistics, 15, 265–286. DOI: 10.1198/106186006X113430.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.