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Nearest comoment estimation with unobserved factors

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Abstract
We propose a minimum distance estimator for the higher-order comoments of a multivariate distribution exhibiting a lower dimensional latent factor structure. We derive the influence function of the proposed estimator and prove its consistency and asymptotic normality. The simulation study confirms the large gains in accuracy compared to the traditional sample comoments. The empirical usefulness of the novel framework is shown in applications to portfolio allocation under non-Gaussian objective functions and to the extraction of factor loadings in a dataset with mental ability scores.
Keywords
Economics and Econometrics, Applied Mathematics

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Please use this url to cite or link to this publication:

MLA
Boudt, Kris, et al. “Nearest Comoment Estimation with Unobserved Factors.” JOURNAL OF ECONOMETRICS, 2020.
APA
Boudt, K., Cornilly, D., & Verdonck, T. (2020). Nearest comoment estimation with unobserved factors. JOURNAL OF ECONOMETRICS.
Chicago author-date
Boudt, Kris, Dries Cornilly, and Tim Verdonck. 2020. “Nearest Comoment Estimation with Unobserved Factors.” JOURNAL OF ECONOMETRICS.
Chicago author-date (all authors)
Boudt, Kris, Dries Cornilly, and Tim Verdonck. 2020. “Nearest Comoment Estimation with Unobserved Factors.” JOURNAL OF ECONOMETRICS.
Vancouver
1.
Boudt K, Cornilly D, Verdonck T. Nearest comoment estimation with unobserved factors. JOURNAL OF ECONOMETRICS. 2020;
IEEE
[1]
K. Boudt, D. Cornilly, and T. Verdonck, “Nearest comoment estimation with unobserved factors,” JOURNAL OF ECONOMETRICS, 2020.
@article{8642253,
  abstract     = {We propose a minimum distance estimator for the higher-order comoments of a multivariate distribution exhibiting a lower dimensional latent factor structure. We derive the influence function of the proposed estimator and prove its consistency and asymptotic normality. The simulation study confirms the large gains in accuracy compared to the traditional sample comoments. The empirical usefulness of the novel framework is shown in applications to portfolio allocation under non-Gaussian objective functions and to the extraction of factor loadings in a dataset with mental ability scores.},
  author       = {Boudt, Kris and Cornilly, Dries and Verdonck, Tim},
  issn         = {0304-4076},
  journal      = {JOURNAL OF ECONOMETRICS},
  keywords     = {Economics and Econometrics,Applied Mathematics},
  language     = {eng},
  title        = {Nearest comoment estimation with unobserved factors},
  url          = {http://dx.doi.org/10.1016/j.jeconom.2019.12.009},
  year         = {2020},
}

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