IDEAS home Printed from https://ideas.repec.org/p/sce/scecf5/385.html
   My bibliography  Save this paper

Worst-case estimation and asymptotic theory for models with unobservables

Author

Listed:
  • Jose M. Vidal-Sanz
  • Mercedes Esteban-Bravo

Abstract

This paper proposes a worst-case approach for estimating econometric models containing unobservable variables. Worst-case estimators are robust against the averse effects of unobservables and, unlike the classical literature, there are no assumptions made about the statistical nature of the unobservables. This method should be seen as complementing standard methods; cautious modelers should compare different estimates to determine robust models. Limiting theory is obtained, and a Monte Carlo study of finite-sample properties is conducted. An economic application is included

Suggested Citation

  • Jose M. Vidal-Sanz & Mercedes Esteban-Bravo, 2005. "Worst-case estimation and asymptotic theory for models with unobservables," Computing in Economics and Finance 2005 385, Society for Computational Economics.
    • Handle: RePEc:sce:scecf5:385
    as

    Download full text from publisher

    File URL: http://ideas.repec.org/p/cte/wbrepe/wb045518.html
    File Function: main text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Amemiya, Yasuo, 1985. "Instrumental variable estimator for the nonlinear errors-in-variables model," Journal of Econometrics, Elsevier, vol. 28(3), pages 273-289, June.
    2. Aigner, Dennis, 1974. "An Appropriate Econometric Framework for Estimating a Labor Supply Function from the SEO File," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(1), pages 59-68, February.
    3. Chamberlain, Gary, 1977. "Education, income, and ability revisited," Journal of Econometrics, Elsevier, vol. 5(2), pages 241-257, March.
    4. H.J. Bierens, 1981. "Robust Methods and Asymptotic Theory in Nonlinear Econometrics," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 35(3), pages 173-173, September.
    5. Chamberlain, Gary & Griliches, Zvi, 1975. "Unobservables with a Variance-Components Structure: Ability, Schooling, and the Economic Success of Brothers," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 16(2), pages 422-449, June.
    6. Aigner, Dennis J. & Hsiao, Cheng & Kapteyn, Arie & Wansbeek, Tom, 1984. "Latent variable models in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 23, pages 1321-1393, Elsevier.
    Full references (including those not matched with items on IDEAS)

    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. Ben-Moshe, Dan, 2018. "Identification Of Joint Distributions In Dependent Factor Models," Econometric Theory, Cambridge University Press, vol. 34(1), pages 134-165, February.
    2. Carneiro, Pedro & Hansen, Karsten & Heckman, James, 2003. "Estimating distributions of treatment effects with an application to the returns to schooling and measurement of the effects of uncertainty on college choice," Working Paper Series 2003:9, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    3. Hausman, J. A. & Newey, W. K. & Powell, J. L., 1995. "Nonlinear errors in variables Estimation of some Engel curves," Journal of Econometrics, Elsevier, vol. 65(1), pages 205-233, January.
    4. Hahn, Jinyong & Hausman, Jerry & Kim, Jeonghwan, 2021. "A small sigma approach to certain problems in errors-in-variables models," Economics Letters, Elsevier, vol. 208(C).
    5. Wang, Liqun & Hsiao, Cheng, 2011. "Method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models," Journal of Econometrics, Elsevier, vol. 165(1), pages 30-44.
    6. Mark M. Pitt & Shahidur R. Khandker & Omar Haider Chowdhury & Daniel L. Millimet, 1998. "Credit Programs for the Poor and the Nutritional Status of Children in Rural Bangladesh," Working Papers 98-4, Brown University, Department of Economics, revised 16 Jan 1998.
    7. Jerry Hausman, 2001. "Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 57-67, Fall.
    8. Pedro Carneiro & Karsten T. Hansen & James J. Heckman, 2003. "Estimating Distributions of Treatment Effects with an Application to the Returns to Schooling and Measurement of the Effects of Uncertainty on College," NBER Working Papers 9546, National Bureau of Economic Research, Inc.
    9. Francesco Agostinelli & Matthew Wiswall, 2016. "Estimating the Technology of Children's Skill Formation," NBER Working Papers 22442, National Bureau of Economic Research, Inc.
    10. French, Eric Baird & O’Dea, Cormac & MacCuish, Jamie, 2021. "The Intergenerational Elasticity of Earnings: Exploring the Mechanisms," CEPR Discussion Papers 15975, C.E.P.R. Discussion Papers.
    11. Francesco Agostinelli & Matthew Wiswall, 2016. "Identification of Dynamic Latent Factor Models: The Implications of Re-Normalization in a Model of Child Development," NBER Working Papers 22441, National Bureau of Economic Research, Inc.
    12. Sule Alan & Orazio Attanasio & Martin Browning, 2009. "Estimating Euler equations with noisy data: two exact GMM estimators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(2), pages 309-324, March.
    13. repec:wyi:journl:002076 is not listed on IDEAS
    14. Stoker, Thomas M. & Berndt, Ernst R. & Denny Ellerman, A. & Schennach, Susanne M., 2005. "Panel data analysis of U.S. coal productivity," Journal of Econometrics, Elsevier, vol. 127(2), pages 131-164, August.
    15. Joop Hartog & Luis Díaz-Serrano, 2007. "Earnings risk and demand for higher education: A cross-section test for Spain," Journal of Applied Economics, Universidad del CEMA, vol. 10, pages 1-28, May.
    16. Brumm, Harold J, 2000. "Inflation and Central Bank Independence: Conventional Wisdom Redux," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 32(4), pages 807-819, November.
    17. Orley Ashenfelter & Cecilia Rouse, 1998. "Income, Schooling, and Ability: Evidence from a New Sample of Identical Twins," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(1), pages 253-284.
    18. Christopher R. Bollinger, 2001. "Response Error and the Union Wage Differential," Southern Economic Journal, John Wiley & Sons, vol. 68(1), pages 60-76, July.
    19. Yingyao Hu & Yang Liu & Jiaxiong Yao, 2022. "Revealing Unobservables by Deep Learning: Generative Element Extraction Networks (GEEN)," Papers 2210.01300, arXiv.org.
    20. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
    21. Di Tommaso, Maria Laura & Maccagnan, Anna & Mendolia, Silvia, 2018. "The Gender Gap in Attitudes and Test Scores: A New Construct of the Mathematical Capability," IZA Discussion Papers 11843, Institute of Labor Economics (IZA).

    More about this item

    Keywords

    unobservable variables; robust estimation; minimax optimization; M-estimators; GMM-estimators;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:sce:scecf5:385. 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: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/sceeeea.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.