IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp12576.html
   My bibliography  Save this paper

Accounting for Skewed or One-Sided Measurement Error in the Dependent Variable

Author

Listed:
  • Millimet, Daniel L.

    (Southern Methodist University)

  • Parmeter, Christopher F.

    (University of Miami)

Abstract

While classical measurement error in the dependent variable in a linear regression framework results only in a loss of precision, non-classical measurement error can lead to estimates which are biased and inference which lacks power. Here, we consider a particular type of non-classical measurement error: skewed errors. Unfortunately, skewed measurement error is likely to be a relatively common feature of many outcomes of interest in political science research. This study highlights the bias that can result even from relatively "small" amounts of skewed measurement error, particularly if the measurement error is heteroskedastic. We also assess potential solutions to this problem, focusing on the stochastic frontier model and nonlinear least squares. Simulations and three replications highlight the importance of thinking carefully about skewed measurement error, as well as appropriate solutions.

Suggested Citation

  • Millimet, Daniel L. & Parmeter, Christopher F., 2019. "Accounting for Skewed or One-Sided Measurement Error in the Dependent Variable," IZA Discussion Papers 12576, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp12576
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp12576.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Richard A. Hofler & John A. List, 2004. "Valuation on the Frontier: Calibrating Actual and Hypothetical Statements of Value," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(1), pages 213-221.
    2. Amsler, Christine & Prokhorov, Artem & Schmidt, Peter, 2016. "Endogeneity in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 190(2), pages 280-288.
    3. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
    4. Sebastian Galiani & Martín A. Rossi & Ernesto Schargrodsky, 2011. "Conscription and Crime: Evidence from the Argentine Draft Lottery," American Economic Journal: Applied Economics, American Economic Association, vol. 3(2), pages 119-136, April.
    5. Hung-jen Wang & Peter Schmidt, 2002. "One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels," Journal of Productivity Analysis, Springer, vol. 18(2), pages 129-144, September.
    6. Madhav Joshi & Subodh Raj Pyakurel, 2015. "Individual-Level Data on the Victims of Nepal’s Civil War, 1996--2006: A New Data Set," International Interactions, Taylor & Francis Journals, vol. 41(3), pages 601-619, May.
    7. Quy-Toan Do & Lakshmi Iyer, 2010. "Geography, poverty and conflict in Nepal," Journal of Peace Research, Peace Research Institute Oslo, vol. 47(6), pages 735-748, November.
    8. Wang, Hung-Jen & Ho, Chia-Wen, 2010. "Estimating fixed-effect panel stochastic frontier models by model transformation," Journal of Econometrics, Elsevier, vol. 157(2), pages 286-296, August.
    9. Mani Nepal & Alok K. Bohara & Kishore Gawande, 2011. "More Inequality, More Killings: The Maoist Insurgency in Nepal," American Journal of Political Science, John Wiley & Sons, vol. 55(4), pages 886-906, October.
    10. Caudill, Steven B & Ford, Jon M & Gropper, Daniel M, 1995. "Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 105-111, January.
    11. Kosuke Imai & Teppei Yamamoto, 2010. "Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analysis," American Journal of Political Science, John Wiley & Sons, vol. 54(2), pages 543-560, April.
    12. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    13. Goel, Rajeev K & Nelson, Michael A, 1998. "Corruption and Government Size: A Disaggregated Analysis," Public Choice, Springer, vol. 97(1-2), pages 107-120, October.
    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. Fuest, Clemens & Hugger, Felix & Neumeier, Florian, 2022. "Corporate profit shifting and the role of tax havens: Evidence from German country-by-country reporting data," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 454-477.
    2. Delis, Manthos D. & Dioikitopoulos, Evangelos V. & Ongena, Steven, 2023. "Population diversity and financial risk-taking," Journal of Banking & Finance, Elsevier, vol. 151(C).

    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. Orea, Luis, 2019. "The Econometric Measurement of Firms’ Efficiency," Efficiency Series Papers 2019/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    2. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    3. Llorca, Manuel & Orea, Luis & Pollitt, Michael G., 2016. "Efficiency and environmental factors in the US electricity transmission industry," Energy Economics, Elsevier, vol. 55(C), pages 234-246.
    4. Martinez-Cillero, Maria & Lawless, Martina & O'Toole, Conor, 2023. "Analysing SME investment, financing constraints and its determinants. A stochastic frontier approach," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 578-588.
    5. Ali M. Oumer & Amin Mugera & Michael Burton & Atakelty Hailu, 2022. "Technical efficiency and firm heterogeneity in stochastic frontier models: application to smallholder maize farms in Ethiopia," Journal of Productivity Analysis, Springer, vol. 57(2), pages 213-241, April.
    6. Oleg Badunenko & Daniel J. Henderson, 2024. "Production analysis with asymmetric noise," Journal of Productivity Analysis, Springer, vol. 61(1), pages 1-18, February.
    7. Paul, Satya & Shankar, Sriram, 2018. "Modelling Efficiency Effects in a True Fixed Effects Stochastic Frontier," MPRA Paper 87437, University Library of Munich, Germany.
    8. Satya Paul & Sriram Shankar, 2020. "Estimating efficiency effects in a panel data stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 53(2), pages 163-180, April.
    9. Christopher F. Parmeter & Hung-Jen Wang & Subal C. Kumbhakar, 2017. "Nonparametric estimation of the determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 47(3), pages 205-221, June.
    10. Amsler, Christine & Prokhorov, Artem & Schmidt, Peter, 2017. "Endogenous environmental variables in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 199(2), pages 131-140.
    11. Hung-Jen Wang, 2002. "Heteroscedasticity and Non-Monotonic Efficiency Effects of a Stochastic Frontier Model," Journal of Productivity Analysis, Springer, vol. 18(3), pages 241-253, November.
    12. Gralka, Sabine, 2018. "Stochastic frontier analysis in higher education: A systematic review," CEPIE Working Papers 05/18, Technische Universität Dresden, Center of Public and International Economics (CEPIE).
    13. Martini, Gianmaria & Scotti, Davide & Viola, Domenico & Vittadini, Giorgio, 2020. "Persistent and temporary inefficiency in airport cost function: An application to Italy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 999-1019.
    14. Curtiss, Jarmila & Brümmer, Bernhard & Medonos, Tomas & Weaver, Robert D., 2005. "Structural Change in Transition: A Role for Organizational Legitimacy? Evidence from Czech Agriculture," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24557, European Association of Agricultural Economists.
    15. Belotti, Federico & Ilardi, Giuseppe, 2018. "Consistent inference in fixed-effects stochastic frontier models," Journal of Econometrics, Elsevier, vol. 202(2), pages 161-177.
    16. Taining Wang & Jinjing Tian & Feng Yao, 2021. "Does high debt ratio influence Chinese firms’ performance? A semiparametric stochastic frontier approach with zero inefficiency," Empirical Economics, Springer, vol. 61(2), pages 587-636, August.
    17. Rao, Xudong, 2014. "Land Fragmentation with Double Bonuses -- The Case of Tanzanian Agriculture," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169436, Agricultural and Applied Economics Association.
    18. Kai Sun & Ruhul Salim, 2020. "A semiparametric stochastic input distance frontier model with application to the Indonesian banking industry," Journal of Productivity Analysis, Springer, vol. 54(2), pages 139-156, December.
    19. repec:kap:iaecre:v:14:y:2008:i:1:p:76-89 is not listed on IDEAS
    20. Mamonov Mikhail E. & Parmeter Christopher F. & Prokhorov Artem B., 2022. "Dependence modeling in stochastic frontier analysis," Dependence Modeling, De Gruyter, vol. 10(1), pages 123-144, January.
    21. Barra, Cristian & Lagravinese, Raffaele & Zotti, Roberto, 2018. "Does econometric methodology matter to rank universities? An analysis of Italian higher education system," Socio-Economic Planning Sciences, Elsevier, vol. 62(C), pages 104-120.

    More about this item

    Keywords

    stochastic frontier; nonlinear least squares; measurement error;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    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:iza:izadps:dp12576. 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: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.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.