IDEAS home Printed from https://ideas.repec.org/p/eie/wpaper/1505.html
   My bibliography  Save this paper

On the ambiguous consequences of omitting variables

Author

Listed:
  • Giuseppe De Luca

    (University of Palermo)

  • Jan R. Magnus

    (VU University Amsterdam and Tinbergen Institute)

  • Franco Peracchi

    (University of Rome "Tor Vergata" and EIEF)

Abstract

This paper studies what happens when we move from a short regression to a long regression (or vice versa), when the long regression is shorter than the data-generation process. In the special case where the long regression equals the data-generation process, the least-squares estimators have smaller bias (in fact zero bias) but larger variances in the long regression than in the short regression. But if the long regression is also misspecified, the bias may not be smaller. We provide bias and mean squared error comparisons and study the dependence of the differences on the misspecification parameter.

Suggested Citation

  • Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2015. "On the ambiguous consequences of omitting variables," EIEF Working Papers Series 1505, Einaudi Institute for Economics and Finance (EIEF), revised May 2015.
    • Handle: RePEc:eie:wpaper:1505
    as

    Download full text from publisher

    File URL: http://www.eief.it/files/2015/05/wp-05-on-the-ambiguous-consequences-of-omitting-variables.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Frost, Peter A, 1979. "Proxy Variables and Specification Bias," The Review of Economics and Statistics, MIT Press, vol. 61(2), pages 323-325, May.
    2. Hansen, Bruce E., 2005. "Challenges For Econometric Model Selection," Econometric Theory, Cambridge University Press, vol. 21(1), pages 60-68, February.
    3. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    4. Kinal, Terrence & Lahiri, Kajal, 1983. "Specification Error Analysis with Stochastic Regressors," Econometrica, Econometric Society, vol. 51(4), pages 1209-1219, July.
    5. Cameron,A. Colin & Trivedi,Pravin K., 2005. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9780521848053, January.
    6. Jan R. Magnus & Andrey L. Vasnev, 2007. "Local sensitivity and diagnostic tests," Econometrics Journal, Royal Economic Society, vol. 10(1), pages 166-192, March.
    7. Joshua D. Angrist & Jörn-Steffen Pischke, 2015. "The path from cause to effect: mastering 'metrics," CentrePiece - The magazine for economic performance 442, Centre for Economic Performance, LSE.
    8. McCallum, B T, 1972. "Relative Asymptotic Bias from Errors of Omission and Measurement," Econometrica, Econometric Society, vol. 40(4), pages 757-758, July.
    9. Jan R. Magnus & J. Durbin, 1999. "Estimation of Regression Coefficients of Interest When Other Regression Coefficients Are of No Interest," Econometrica, Econometric Society, vol. 67(3), pages 639-644, May.
    10. Jan R. Magnus & Giuseppe De Luca, 2016. "Weighted-Average Least Squares (Wals): A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 30(1), pages 117-148, February.
    11. Holly, Alberto, 1982. "A Remark on Hausman's Specification Test," Econometrica, Econometric Society, vol. 50(3), pages 749-759, May.
    12. Garber, Steven & Klepper, Steven, 1980. "Extending the Classical Normal Errors-in-Variables Model," Econometrica, Econometric Society, vol. 48(6), pages 1541-1546, September.
    13. Kevin A. Clarke, 2005. "The Phantom Menace: Omitted Variable Bias in Econometric Research," Conflict Management and Peace Science, Peace Science Society (International), vol. 22(4), pages 341-352, September.
    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. Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2018. "Balanced Variable Addition In Linear Models," Journal of Economic Surveys, Wiley Blackwell, vol. 32(4), pages 1183-1200, September.
    2. Adrian C. Darnell, 1994. "A Dictionary Of Econometrics," Books, Edward Elgar Publishing, number 118.
    3. De Luca, Giuseppe & Magnus, Jan R. & Peracchi, Franco, 2018. "Weighted-average least squares estimation of generalized linear models," Journal of Econometrics, Elsevier, vol. 204(1), pages 1-17.
    4. Giuseppe Luca & Jan R. Magnus & Franco Peracchi, 2023. "Weighted-Average Least Squares (WALS): Confidence and Prediction Intervals," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1637-1664, April.
    5. Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2019. "Comments on “Unobservable Selection and Coefficient Stability: Theory and Evidence” and “Poorly Measured Confounders are More Useful on the Left Than on the Right”," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(2), pages 217-222, April.
    6. Judith Anne Clarke, 2017. "Model Averaging OLS and 2SLS: An Application of the WALS Procedure," Econometrics Working Papers 1701, Department of Economics, University of Victoria.
    7. Doko Tchatoka, Firmin Sabro, 2012. "Specification Tests with Weak and Invalid Instruments," MPRA Paper 40185, University Library of Munich, Germany.
    8. Lisa Cook, 2014. "Violence and economic activity: evidence from African American patents, 1870–1940," Journal of Economic Growth, Springer, vol. 19(2), pages 221-257, June.
    9. Davidson, Russell & MacKinnon, James G., 1989. "Testing for Consistency using Artificial Regressions," Econometric Theory, Cambridge University Press, vol. 5(3), pages 363-384, December.
    10. O'Brien, Raymond & Patacchini, Eleonora, 2003. "Testing the exogeneity assumption in panel data models with "non classical" disturbances," Discussion Paper Series In Economics And Econometrics 0302, Economics Division, School of Social Sciences, University of Southampton.
    11. Patrick Alves & Alexandre Messa Silva, 2008. "Estimativa do Estoque de Capital das Empresas Industriais Brasileiras," Discussion Papers 1325, Instituto de Pesquisa Econômica Aplicada - IPEA.
    12. Zheng, Yanfeng & Liu, Jing & George, Gerard, 2010. "The dynamic impact of innovative capability and inter-firm network on firm valuation: A longitudinal study of biotechnology start-ups," Journal of Business Venturing, Elsevier, vol. 25(6), pages 593-609, November.
    13. Mayston, David, 2009. "The determinants of cumulative endogeneity bias in multivariate analysis," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1120-1136, July.
    14. Doko Tchatoka, Firmin & Dufour, Jean-Marie, 2020. "Exogeneity tests, incomplete models, weak identification and non-Gaussian distributions: Invariance and finite-sample distributional theory," Journal of Econometrics, Elsevier, vol. 218(2), pages 390-418.
    15. Erik Mellander & Sofia Sandgren‐Massih, 2008. "Proxying Ability by Family Background in Returns to Schooling Estimations is Generally a Bad Idea," Scandinavian Journal of Economics, Wiley Blackwell, vol. 110(4), pages 853-875, December.
    16. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
    17. John Geweke & Joel Horowitz & M. Hashem Pesaran, 2006. "Econometrics: A Bird’s Eye View," CESifo Working Paper Series 1870, CESifo.
    18. Jean-Paul Azam & Kartika Bhatia, 2017. "Provoking insurgency in a federal state: theory and application to India," Public Choice, Springer, vol. 170(3), pages 183-210, March.
    19. Migliardo, Carlo, 2012. "Heterogeneity in price setting behavior, spatial disparities and sectoral diversity: Evidence from a panel of Italian firms," Economic Modelling, Elsevier, vol. 29(4), pages 1106-1118.
    20. Jiao, Xiyu & Pretis, Felix & Schwarz, Moritz, 2024. "Testing for coefficient distortion due to outliers with an application to the economic impacts of climate change," Journal of Econometrics, Elsevier, vol. 239(1).

    More about this item

    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:eie:wpaper:1505. 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: Facundo Piguillem (email available below). General contact details of provider: https://edirc.repec.org/data/einauit.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.