IDEAS home Printed from https://ideas.repec.org/a/bla/istatr/v91y2023i1p55-71.html
   My bibliography  Save this article

Diagnostic Tests for the Necessity of Weight in Regression With Survey Data

Author

Listed:
  • Feng Wang
  • HaiYing Wang
  • Jun Yan

Abstract

To weight or not to weight in regression analyses with survey data has been debated in the literature. The problem is essentially a tradeoff between the bias and the variance of the regression coefficient estimator. An array of diagnostic tests for informative weights have been developed. Nonetheless, studies comparing the performance of the tests, especially for finite samples, are scarce, and the theoretical equivalence of some tests has not been investigated. Focusing on the linear regression setting, we review a collection of such tests and propose enhanced versions of some of them that require an auxiliary regression model for the weight. Further, the equivalence of two popular tests is established which has not been reported before. In contrast to existing reviews with no empirical comparison, we compare the sizes and powers of the tests in simulation studies. The reviewed tests are applied to a regression analysis of the family expenditure using the data from the China Family Panel Study.

Suggested Citation

  • Feng Wang & HaiYing Wang & Jun Yan, 2023. "Diagnostic Tests for the Necessity of Weight in Regression With Survey Data," International Statistical Review, International Statistical Institute, vol. 91(1), pages 55-71, April.
    • Handle: RePEc:bla:istatr:v:91:y:2023:i:1:p:55-71
    DOI: 10.1111/insr.12509
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/insr.12509
    Download Restriction: no
    File URL: https://libkey.io/10.1111/insr.12509?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Konstantin Gluschenko, 2018. "Measuring regional inequality: to weight or not to weight?," Spatial Economic Analysis, Taylor & Francis Journals, vol. 13(1), pages 36-59, January.
    2. Gary Solon & Steven J. Haider & Jeffrey M. Wooldridge, 2015. "What Are We Weighting For?," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 301-316.
    3. Sophia Rabe‐Hesketh & Anders Skrondal, 2006. "Multilevel modelling of complex survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 805-827, October.
    4. Xiaochen Ma & Ziyue Wang & Xiaoyun Liu, 2019. "Progress on Catastrophic Health Expenditure in China: Evidence from China Family Panel Studies (CFPS) 2010 to 2016," IJERPH, MDPI, vol. 16(23), pages 1-11, November.
    5. Lumley, Thomas, 2004. "Analysis of Complex Survey Samples," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i08).
    6. Kott, Phillip S., 1991. "What Does Performing Linear Regression on Sample Survey Data Mean?," Journal of Agricultural Economics Research, United States Department of Agriculture, Economic Research Service, vol. 43(1), pages 1-4.
    7. 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.
    8. Chang-ming Hsieh, 2004. "To Weight or not to Weight: The Role of Domain Importance in Quality of Life Measurement," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 68(2), pages 163-174, September.
    9. Pfeffermann, Danny & Sverchkov, Michail, 2007. "Small-Area Estimation Under Informative Probability Sampling of Areas and Within the Selected Areas," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1427-1439, December.
    10. Nancy Duong Nguyen & Patrick Murphy, 2015. "To Weight or Not To Weight? A Statistical Analysis of How Weights Affect the Reliability of the Quarterly National Household Survey for Immigration Research in Ireland," The Economic and Social Review, Economic and Social Studies, vol. 46(4), pages 567-603.
    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. Ana Maria Osorio & Catalina Bolancé & Nyovane Madise & Katharina Rathmann, 2013. "Social Determinants of Child Health in Colombia: Can Community Education Moderate the Effect of Family Characteristics?," Working Papers XREAP2013-02, Xarxa de Referència en Economia Aplicada (XREAP), revised Mar 2013.
    2. Sloczynski, Tymon, 2018. "A General Weighted Average Representation of the Ordinary and Two-Stage Least Squares Estimands," IZA Discussion Papers 11866, Institute of Labor Economics (IZA).
    3. Sheyla Rodrigues Cassy & Samuel Manda & Filipe Marques & Maria do Rosário Oliveira Martins, 2022. "Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique," IJERPH, MDPI, vol. 19(10), pages 1-15, May.
    4. Tymon S{l}oczy'nski, 2018. "Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights," Papers 1810.01576, arXiv.org, revised May 2020.
    5. Joseph L Dieleman & Tara Templin, 2014. "Random-Effects, Fixed-Effects and the within-between Specification for Clustered Data in Observational Health Studies: A Simulation Study," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-17, October.
    6. Marco Stringa & Allan Monks, 2007. "Inter-industry contagion between UK life insurers and UK banks: an event study," Bank of England working papers 325, Bank of England.
    7. Cabral, Joilson de Assis & Freitas Cabral, Maria Viviana de & Pereira Júnior, Amaro Olímpio, 2020. "Elasticity estimation and forecasting: An analysis of residential electricity demand in Brazil," Utilities Policy, Elsevier, vol. 66(C).
    8. Fabrizio Rossi & Maretno Agus Harjoto, 2020. "Corporate non-financial disclosure, firm value, risk, and agency costs: evidence from Italian listed companies," Review of Managerial Science, Springer, vol. 14(5), pages 1149-1181, October.
    9. Andrea Boitani & Marcella Nicolini & Carlo Scarpa, 2013. "Do competition and ownership matter? Evidence from local public transport in Europe," Applied Economics, Taylor & Francis Journals, vol. 45(11), pages 1419-1434, April.
    10. Stefano Magrini & Margherita Gerolimetto & Hasan Engin Duran, 2011. "Understanding the lead/lag structure among regional business cycles," Working Papers 2011_06, Department of Economics, University of Venice "Ca' Foscari".
    11. Hanson, Gordon H., 2001. "U.S.-Mexico Integration and Regional Economies: Evidence from Border-City Pairs," Journal of Urban Economics, Elsevier, vol. 50(2), pages 259-287, September.
    12. Dutta, Dilip & Ghosh, Paritosh Chandra, 2003. "Re-examining Economic Growth-Environment Relationship: Evidence from High-, Medium- And Low-Income Countries," Working Papers 3, University of Sydney, School of Economics.
    13. Cooray, Arusha, 2011. "The role of the government in financial sector development," Economic Modelling, Elsevier, vol. 28(3), pages 928-938, May.
    14. Akbar Ullah & Ejaz Ghani & Attiya Y. Javed, 2013. "Market Power and Industrial Performance in Pakistan," PIDE-Working Papers 2013:88, Pakistan Institute of Development Economics.
    15. Jack Hadley & James Reschovsky & James O’Malley & Bruce Landon, 2014. "Factors associated with geographic variation in cost per episode of care for three medical conditions," Health Economics Review, Springer, vol. 4(1), pages 1-19, December.
    16. Campbell, Randall C. & Nagel, Gregory L., 2016. "Private information and limitations of Heckman's estimator in banking and corporate finance research," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 186-195.
    17. Domenico Depalo, 2020. "Explaining the causal effect of adherence to medication on cholesterol through the marginal patient," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 110-126, October.
    18. C, Loran & Eckbo, Espen & Lu, Ching-Chih, 2014. "Does Executive Compensation Reflect Default Risk?," UiS Working Papers in Economics and Finance 2014/11, University of Stavanger.
    19. Jean-Paul Azam & Kartika Bhatia, 2017. "Provoking Insurgency in a Federal State: Theory and Application to India," Post-Print hal-04449321, HAL.
    20. Giraitis, Liudas & Kapetanios, George & Marcellino, Massimiliano, 2021. "Time-varying instrumental variable estimation," Journal of Econometrics, Elsevier, vol. 224(2), pages 394-415.

    More about this item

    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:bla:istatr:v:91:y:2023:i:1:p:55-71. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/isiiinl.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.