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Consistent Noisy Independent Component Analysis

Author

Listed:
  • Stéphane Bonhomme

    (CEMFI - Centre de Estudios monetarios y financierios - Banco de España)

  • Jean-Marc Robin

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

We study linear factor models under the assumptions that factors are mutually independent and independent of errors, and errors can be correlated to some extent. Under the factor non-Gaussianity, second-to-fourth-order moments are shown to yield full identification of the matrix of factor loadings. We develop a simple algorithm to estimate the matrix of factor loadings from these moments. We run Monte Carlo simulations and apply our methodology to data on cognitive test scores, and financial data on stock returns.

Suggested Citation

  • Stéphane Bonhomme & Jean-Marc Robin, 2009. "Consistent Noisy Independent Component Analysis," PSE-Ecole d'économie de Paris (Postprint) hal-00642732, HAL.
    • Handle: RePEc:hal:pseptp:hal-00642732
    DOI: 10.1016/j.jeconom.2008.12.019
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    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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    Cited by:

    1. Neto, David, 2022. "Revisiting spillovers between investor attention and cryptocurrency markets using noisy independent component analysis and transfer entropy," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    2. Susanne M. Schennach, 2012. "Measurement error in nonlinear models - a review," CeMMAP working papers CWP41/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Oliver Cassagneau-Francis, 2022. "Revisiting the Returns to Higher Education: Heterogeneity by Cognitive and Non-Cognitive Abilities," Working Papers hal-04067399, HAL.
    4. Sascha A. Keweloh, 2023. "Uncertain Short-Run Restrictions and Statistically Identified Structural Vector Autoregressions," Papers 2303.13281, arXiv.org, revised Apr 2024.
    5. Matteo Barigozzi & Alessio Moneta, 2016. "Identifying the Independent Sources of Consumption Variation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(2), pages 420-449, March.
    6. Brandts, Jordi & El Baroudi, Sabrine & Huber, Stefanie J. & Rott, Christina, 2021. "Gender differences in private and public goal setting," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 222-247.
    7. Stéphane Bonhomme & Koen Jochmans & Jean-Marc Robin, 2013. "Nonparametric estimation of finite mixtures," SciencePo Working papers hal-00972868, HAL.
    8. Benjamin Williams, 2018. "Identification of the Linear Factor Model," Working Papers 2018-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    9. Gouriéroux, Christian & Monfort, Alain & Renne, Jean-Paul, 2017. "Statistical inference for independent component analysis: Application to structural VAR models," Journal of Econometrics, Elsevier, vol. 196(1), pages 111-126.
    10. repec:hal:spmain:info:hdl:2441/7o52iohb7k6srk09n8t4k21sm is not listed on IDEAS
    11. Jane Cooley Fruehwirth & Salvador Navarro & Yuya Takahashi, 2016. "How the Timing of Grade Retention Affects Outcomes: Identification and Estimation of Time-Varying Treatment Effects," Journal of Labor Economics, University of Chicago Press, vol. 34(4), pages 979-1021.
    12. Guay, Alain, 2021. "Identification of structural vector autoregressions through higher unconditional moments," Journal of Econometrics, Elsevier, vol. 225(1), pages 27-46.
    13. Stéphane Bonhomme & Koen Jochmans & Jean-Marc Robin, 2014. "Nonparametric estimation of finite measures," CeMMAP working papers CWP11/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Heckman, James J. & Humphries, John Eric & Veramendi, Gregory, 2016. "Dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 191(2), pages 276-292.
    15. Lukas Hoesch & Adam Lee & Geert Mesters, 2022. "Robust inference for non-Gaussian SVAR models," Economics Working Papers 1847, Department of Economics and Business, Universitat Pompeu Fabra.
    16. Lukas Hoesch & Adam Lee & Geert Mesters, 2022. "Locally Robust Inference for Non-Gaussian SVAR Models," Working Papers 1367, Barcelona School of Economics.
    17. Boudt, Kris & Cornilly, Dries & Verdonck, Tim, 2020. "Nearest comoment estimation with unobserved factors," Journal of Econometrics, Elsevier, vol. 217(2), pages 381-397.
    18. Virta, Joni & Li, Bing & Nordhausen, Klaus & Oja, Hannu, 2020. "Independent component analysis for multivariate functional data," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
    19. Oliver Cassagneau-Francis, 2022. "Revisiting the Returns to Higher Education: Heterogeneity by Cognitive and Non-Cognitive Abilities," SciencePo Working papers Main hal-04067399, HAL.
    20. repec:hal:wpspec:info:hdl:2441/7o52iohb7k6srk09n8t4k21sm is not listed on IDEAS
    21. Ben-Moshe, Dan, 2018. "Identification Of Joint Distributions In Dependent Factor Models," Econometric Theory, Cambridge University Press, vol. 34(1), pages 134-165, February.

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