IDEAS home Printed from https://ideas.repec.org/a/taf/amstat/v71y2017i2p97-105.html
   My bibliography  Save this article

Reversals of Least-Square Estimates and Model-Invariant Estimation for Directions of Unique Effects

Author

Listed:
  • Brian Knaeble
  • Seth Dutter

Abstract

When a linear model is adjusted to control for additional explanatory variables, the sign of a fitted coefficient may reverse. Here, these reversals are studied using coefficients of determination. The resulting theory can be used to determine directions of unique effects in the presence of model uncertainty. This process is called model-invariant estimation when the estimates are invariant across changes to the model structure. When a single covariate is added, the reversal region can be understood geometrically as an elliptical cone of two nappes with an axis of symmetry relating to a best-possible condition for a reversal using a single coefficient of determination. When a set of covariates are added to a model with a single explanatory variable, model-invariant estimation can be implemented using subject matter knowledge. More general theory with partial coefficients is applicable to analysis of large datasets. Applications are demonstrated with dietary health data from the United Nations.

Suggested Citation

  • Brian Knaeble & Seth Dutter, 2017. "Reversals of Least-Square Estimates and Model-Invariant Estimation for Directions of Unique Effects," The American Statistician, Taylor & Francis Journals, vol. 71(2), pages 97-105, April.
    • Handle: RePEc:taf:amstat:v:71:y:2017:i:2:p:97-105
    DOI: 10.1080/00031305.2016.1226951
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00031305.2016.1226951
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00031305.2016.1226951?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Guildo W. Imbens, 2003. "Sensitivity to Exogeneity Assumptions in Program Evaluation," American Economic Review, American Economic Association, vol. 93(2), pages 126-132, May.
    2. McAleer, Michael & Pagan, Adrian & Visco, Ignazio, 1986. "A further result on the sign of restricted least-squares estimates," Journal of Econometrics, Elsevier, vol. 32(2), pages 287-290, July.
    3. Visco, Ignazio, 1978. "On obtaining the right sign of a coefficient estimate by omitting a variable from the regression," Journal of Econometrics, Elsevier, vol. 7(1), pages 115-117, February.
    4. Leamer, Edward E., 1975. "A result on the sign of restricted least-squares estimates," Journal of Econometrics, Elsevier, vol. 3(4), pages 387-390, November.
    5. Chris Chatfield, 1995. "Model Uncertainty, Data Mining and Statistical Inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(3), pages 419-444, May.
    6. Kenneth A. Frank, 2000. "Impact of a Confounding Variable on a Regression Coefficient," Sociological Methods & Research, , vol. 29(2), pages 147-194, November.
    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. Eric Craig Watson & Heejun Chang, 2018. "Relation Between Stream Temperature and Landscape Characteristics Using Distance Weighted Metrics," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1167-1192, February.

    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. Yitzhaki, Shlomo, 1990. "On the Sensitivity of a Regression Coefficient to Monotonic Transformations," Econometric Theory, Cambridge University Press, vol. 6(2), pages 165-169, June.
    2. Pesaran, M. Hashem & Smith, Ron P., 2014. "Signs of impact effects in time series regression models," Economics Letters, Elsevier, vol. 122(2), pages 150-153.
    3. Leonardo Becchetti & Pierluigi Conzo & Alessandro Romeo, 2014. "Violence, trust, and trustworthiness: evidence from a Nairobi slum," Oxford Economic Papers, Oxford University Press, vol. 66(1), pages 283-305, January.
    4. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    5. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    6. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    7. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    8. Michael A. Clemens & Claudio Montenegro & Lant Pritchett, 2016. "Bounding the Price Equivalent of Migration Barriers," CID Working Papers 316, Center for International Development at Harvard University.
    9. Mequanint B. Melesse & Amos Nyangira Tirra & Yabibal M. Walle & Michael Hauser, 2023. "Understanding the Determinants of Aspirations in Rural Tanzania: Does Financial Literacy Matter?," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 35(6), pages 1294-1321, December.
    10. Colnet Bénédicte & Josse Julie & Varoquaux Gaël & Scornet Erwan, 2022. "Causal effect on a target population: A sensitivity analysis to handle missing covariates," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 372-414, January.
    11. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2006. "Moving the Goalposts: Addressing Limited Overlap in the Estimation of Average Treatment Effects by Changing the Estimand," NBER Technical Working Papers 0330, National Bureau of Economic Research, Inc.
    12. Battistin, Erich & Chesher, Andrew, 2014. "Treatment effect estimation with covariate measurement error," Journal of Econometrics, Elsevier, vol. 178(2), pages 707-715.
    13. Lee, Yun Shin & Scholtes, Stefan, 2014. "Empirical prediction intervals revisited," International Journal of Forecasting, Elsevier, vol. 30(2), pages 217-234.
    14. Shen, Chung-Hua & Wu, Meng-Wen & Chen, Ting-Hsuan & Fang, Hao, 2016. "To engage or not to engage in corporate social responsibility: Empirical evidence from global banking sector," Economic Modelling, Elsevier, vol. 55(C), pages 207-225.
    15. Coleman, Stephen, 2005. "Testing Theories with Qualitative and Quantitative Predictions," MPRA Paper 105171, University Library of Munich, Germany.
    16. Ciarlone, Alessio & Miceli, Valeria, 2016. "Escaping financial crises? Macro evidence from sovereign wealth funds' investment behaviour," Emerging Markets Review, Elsevier, vol. 27(C), pages 169-196.
    17. Becchetti, Leonardo & Castriota, Stefano & Conzo, Pierluigi, 2017. "Disaster, Aid, and Preferences: The Long-run Impact of the Tsunami on Giving in Sri Lanka," World Development, Elsevier, vol. 94(C), pages 157-173.
    18. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    19. Guido W. Imbens, 2022. "Causality in Econometrics: Choice vs Chance," Econometrica, Econometric Society, vol. 90(6), pages 2541-2566, November.
    20. Matthew A. Masten & Alexandre Poirier, 2020. "Inference on breakdown frontiers," Quantitative Economics, Econometric Society, vol. 11(1), pages 41-111, January.

    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:taf:amstat:v:71:y:2017:i:2:p:97-105. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UTAS20 .

    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.