IDEAS home Printed from https://ideas.repec.org/p/ems/eureir/1346.html
   My bibliography  Save this paper

System Identification by Dynamic Factor Models

Author

Listed:
  • Heij, C.
  • Scherrer, W.
  • Destler, M.

Abstract

This paper concerns the modelling of stochastic processes by means of dynamic factor models. In such models the observed process is decomposed into a structured part called the latent process, and a remainder that is called noise. The observed variables are treated in a symmetric way, so that no distinction between inputs and outputs is required. This motivates the condition that also the prior assumptions on the noise are symmetric in nature. One of the central questions in this paper is how uncertainty about the noise structure translates into non-uniqueness of the possible underlying latent processes. We investigate several possible noise specifications and analyse properties of the resulting class of observationally equivalent factor models. This concerns in particular the characterization of optimal models and properties of continuity and consistency.

Suggested Citation

  • Heij, C. & Scherrer, W. & Destler, M., 1996. "System Identification by Dynamic Factor Models," Econometric Institute Research Papers EI 9501-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:1346
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/1346/feweco19971105130757.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Leamer, Edward E, 1987. "Errors in Variables in Linear Systems," Econometrica, Econometric Society, vol. 55(4), pages 893-909, July.
    2. Heij, C. & Scherrer, W. & Destler, M., 1996. "System Identification by Dynamic Factor Models," Econometric Institute Research Papers EI 9501-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Heij, C. & Scherrer, W., 1996. "Consistency of System Identification by Global Total Least Squares," Econometric Institute Research Papers EI 9635-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Heij, C. & Scherrer, W., 1996. "Behavioural Approximation of Stochastic Processes by Rank Reduced Spectra," Econometric Institute Research Papers EI 9610/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Heij, C. & Scherrer, W. & Destler, M., 1996. "System Identification by Dynamic Factor Models," Econometric Institute Research Papers EI 9501-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    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.
    1. Thierry Magnac & Eric Maurin, 2008. "Partial Identification in Monotone Binary Models: Discrete Regressors and Interval Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 75(3), pages 835-864.
    2. Christian Bontemps & Thierry Magnac & Eric Maurin, 2012. "Set Identified Linear Models," Econometrica, Econometric Society, vol. 80(3), pages 1129-1155, May.
    3. Coën, Alain & Hübner, Georges, 2009. "Risk and performance estimation in hedge funds revisited: Evidence from errors in variables," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 112-125, January.
    4. Hong, Han & Tamer, Elie, 2003. "A simple estimator for nonlinear error in variable models," Journal of Econometrics, Elsevier, vol. 117(1), pages 1-19, November.
    5. Paris, Quirino & Caputo, Michael R., 2004. "Efficient Estimates of a Model of Production and Cost," Working Papers 93742, University of California, Davis, Department of Agricultural and Resource Economics.
    6. Chalak, Karim, 2024. "Nonparametric Gini-Frisch bounds," Journal of Econometrics, Elsevier, vol. 238(1).
    7. Hyslop, Dean R & Imbens, Guido W, 2001. "Bias from Classical and Other Forms of Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 475-481, October.
    8. Francis J. DiTraglia & Camilo Garcia-Jimeno, 2020. "A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models," Papers 2011.07276, arXiv.org.
    9. Hu, Yingyao, 2006. "Bounding parameters in a linear regression model with a mismeasured regressor using additional information," Journal of Econometrics, Elsevier, vol. 133(1), pages 51-70, July.
    10. Magnac, Thierry, 2013. "Identification partielle : méthodes et conséquences pour les applications empiriques," L'Actualité Economique, Société Canadienne de Science Economique, vol. 89(4), pages 233-258, Décembre.
    11. Paris, Quirino & Caputo, Michael R., 2004. "A Nonlinear Generalized Additive Error Model of Production and Cost," Working Papers 93743, University of California, Davis, Department of Agricultural and Resource Economics.
    12. Rachida Hennani & Michel Terraza, 2012. "Value-at-Risk stressée chaotique d’un portefeuille bancaire," Working Papers 12-23, LAMETA, Universtiy of Montpellier, revised Sep 2012.
    13. Heij, C. & Scherrer, W., 1996. "Behavioural Approximation of Stochastic Processes by Rank Reduced Spectra," Econometric Institute Research Papers EI 9610/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. Chalak, Karim & Kim, Daniel, 2020. "Measurement error in multiple equations: Tobin’s q and corporate investment, saving, and debt," Journal of Econometrics, Elsevier, vol. 214(2), pages 413-432.
    15. Sadefo Kamdem, J. & Mbairadjim Moussa, A. & Terraza, M., 2012. "Fuzzy risk adjusted performance measures: Application to hedge funds," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 702-712.
    16. Bent Christensen & Jesper Bagger, 2014. "Wage and Productivity Dispersion: The Roles of Rent Sharing, Labor Quality and Capital Intensity," 2014 Meeting Papers 473, Society for Economic Dynamics.
    17. Jeremy J. Nalewaik, 2014. "Missing Variation in the Great Moderation: Lack of Signal Error and OLS Regression," Finance and Economics Discussion Series 2014-27, Board of Governors of the Federal Reserve System (U.S.).
    18. Wegge, Leon L., 1996. "Local identifiability of the factor analysis and measurement error model parameter," Journal of Econometrics, Elsevier, vol. 70(2), pages 351-382, February.
    19. Carmichael, Benoît & Coën, Alain, 2008. "Asset pricing models with errors-in-variables," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 778-788, September.
    20. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Corrections

    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:ems:eureir:1346. 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: RePub (email available below). General contact details of provider: https://edirc.repec.org/data/feeurnl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.