IDEAS home Printed from https://ideas.repec.org/p/vic/vicewp/0517.html
   My bibliography  Save this paper

The Bias of Elasticity Estimtors in Linear Regression: Some Analytic Results

Author

Abstract

Using small-disturbance expansions, we derive analytic expressions for the bias of the OLS estimator an elasticity in a linear model, both at an individual sample point and at the sample mean. The magnitudes of these biases are illustrated with Australian expenditure data.

Suggested Citation

  • Chen Qian & David E. Giles, 2005. "The Bias of Elasticity Estimtors in Linear Regression: Some Analytic Results," Econometrics Working Papers 0517, Department of Economics, University of Victoria.
    • Handle: RePEc:vic:vicewp:0517
    Note: ISSN 1485-6441
    as

    Download full text from publisher

    File URL: https://www.uvic.ca/socialsciences/economics/_assets/docs/econometrics/ewp0517.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kenneth W. Clements & Xueyan Zhao, 2005. "Economic Aspects of Marijuana," Economics Discussion / Working Papers 05-28, The University of Western Australia, Department of Economics.
    2. Kadane, Joseph B, 1971. "Comparison of k-Class Estimators when the Disturbances are Small," Econometrica, Econometric Society, vol. 39(5), pages 723-737, September.
    3. Ullah, Aman, 2004. "Finite Sample Econometrics," OUP Catalogue, Oxford University Press, number 9780198774488.
    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. Tokgoz, Simla & Traoré, Fousseini, 2023. "Understanding E10 markets in the U.S.: Evidence from spatial data," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 1267-1281.

    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. Kiviet, Jan F. & Phillips, Garry D.A., 2012. "Higher-order asymptotic expansions of the least-squares estimation bias in first-order dynamic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3705-3729.
    2. Yong Bao & Aman Ullah, 2009. "Expectation of Quadratic Forms in Normal and Nonnormal Variables with Econometric Applications," Working Papers 200907, University of California at Riverside, Department of Economics, revised Jun 2009.
    3. Yong Bao & Aman Ullah, 2021. "Analytical Finite Sample Econometrics: From A. L. Nagar to Now," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 17-37, December.
    4. Emma Iglesias & Jean Marie Dufour, 2004. "Finite Sample and Optimal Inference in Possibly Nonstationary ARCH Models with Gaussian and Heavy-Tailed Errors," Econometric Society 2004 North American Summer Meetings 161, Econometric Society.
    5. David E. Giles, 2005. "The Bias of Inequality Measures in Very Small Samples: Some Analytic Results," Econometrics Working Papers 0514, Department of Economics, University of Victoria.
    6. Yu, Jun, 2012. "Bias in the estimation of the mean reversion parameter in continuous time models," Journal of Econometrics, Elsevier, vol. 169(1), pages 114-122.
    7. Campos, Julia & Ericsson, Neil R. & Hendry, David F., 1996. "Cointegration tests in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 70(1), pages 187-220, January.
    8. Naoto Kunitomo, 1979. "Asymptotic Optimality of the Limited Information Maximum Likelihood Estimator in Large Econometric Models," Discussion Papers 503, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    9. Qian Chen & David E. Giles, 2009. "Finite-Sample Properties of the Maximum Likelihood Estimator for the Poisson Regression Model With Random Covariates," Econometrics Working Papers 0907, Department of Economics, University of Victoria.
    10. Kirill S. Evdokimov & Andrei Zeleneev, 2023. "Simple Estimation of Semiparametric Models with Measurement Errors," Papers 2306.14311, arXiv.org, revised Mar 2024.
    11. Plümper, Thomas & Neumayer, Eric, 2009. "Famine Mortality, Rational Political Inactivity, and International Food Aid," World Development, Elsevier, vol. 37(1), pages 50-61, January.
    12. Christian Gourieroux & Joann Jasiak, 2022. "Long Run Risk in Stationary Structural Vector Autoregressive Models," Papers 2202.09473, arXiv.org.
    13. Kiviet, Jan F., 2020. "Testing the impossible: Identifying exclusion restrictions," Journal of Econometrics, Elsevier, vol. 218(2), pages 294-316.
    14. Prokhorov, Artem, 2012. "Second order bias of quasi-MLE for covariance structure models," Economics Letters, Elsevier, vol. 114(2), pages 195-197.
    15. Rodrigo Alfaro, 2008. "Higher Order Properties of the Symmetricallr Normalized Instrumental Variable Estimator," Working Papers Central Bank of Chile 500, Central Bank of Chile.
    16. Yong Bao, 2015. "Should We Demean the Data?," Annals of Economics and Finance, Society for AEF, vol. 16(1), pages 163-171, May.
    17. Peter C.B. Phillips & Ji Hyung Lee, 2012. "VARs with Mixed Roots Near Unity," Cowles Foundation Discussion Papers 1845, Cowles Foundation for Research in Economics, Yale University.
    18. Aman Ullah & Yong Bao & Ru Zhang, 2014. "Moment Approximation for Unit Root Models with Nonnormal Errors," Working Papers 201401, University of California at Riverside, Department of Economics.
    19. Saman Banafti & Tae-Hwy Lee, 2022. "Inferential Theory for Granular Instrumental Variables in High Dimensions," Papers 2201.06605, arXiv.org, revised Sep 2023.
    20. Symeonides Spyridon D. & Karavias Yiannis & Tzavalis Elias, 2017. "Size corrected Significance Tests in Seemingly Unrelated Regressions with Autocorrelated Errors," Journal of Time Series Econometrics, De Gruyter, vol. 9(1), pages 1-41, January.

    More about this item

    Keywords

    Elasticity; bias; small-distrurbance asymptotics;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

    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:vic:vicewp:0517. 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: Kali Moon (email available below). General contact details of provider: https://edirc.repec.org/data/devicca.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.