IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/18252.html
   My bibliography  Save this paper

Log Odds and Ends

Author

Listed:
  • Edward C. Norton

Abstract

Although independent unobserved heterogeneity--variables that affect the dependent variable but are independent from the other explanatory variables of interest--do not affect the point estimates or marginal effects in least squares regression, they do affect point estimates in nonlinear models such as logit and probit models. In these nonlinear models, independent unobserved heterogeneity changes the arbitrary normalization of the coefficients through the error variance. Therefore, any statistics derived from the estimated coefficients change when additional, seemingly irrelevant, variables are added to the model. Odds ratios must be interpreted as conditional on the data and model. There is no one odds ratio; each odds ratio estimated in a multivariate model is conditional on the data and model in a way that makes comparisons with other results difficult or impossible. This paper provides new Monte Carlo and graphical insights into why this is true, and new understanding of how to interpret fixed effects models, including case control studies. Marginal effects are largely unaffected by unobserved heterogeneity in both linear regression and nonlinear models, including logit and probit and their multinomial and ordered extensions.

Suggested Citation

  • Edward C. Norton, 2012. "Log Odds and Ends," NBER Working Papers 18252, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:18252
    Note: HE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w18252.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Angrist, Joshua D, 2001. "Estimations of Limited Dependent Variable Models with Dummy Endogenous Regressors: Simple Strategies for Empirical Practice," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 2-16, January.
    2. Terza, Joseph V. & Basu, Anirban & Rathouz, Paul J., 2008. "Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling," Journal of Health Economics, Elsevier, vol. 27(3), pages 531-543, May.
    3. Yatchew, Adonis & Griliches, Zvi, 1985. "Specification Error in Probit Models," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 134-139, February.
    4. Thomas A. Mroz & Yaraslau V. Zayats, 2008. "Arbitrarily Normalized Coefficients, Information Sets, and False Reports of "Biases" in Binary Outcome Models," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 406-413, August.
    5. Angrist, Joshua D, 2001. "Estimations of Limited Dependent Variable Models with Dummy Endogenous Regressors: Simple Strategies for Empirical Practice: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 27-28, January.
    6. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," Review of Economic Studies, Oxford University Press, vol. 47(1), pages 225-238.
    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. Maclean, Johanna Catherine & Popovici, Ioana & French, Michael T., 2016. "Are natural disasters in early childhood associated with mental health and substance use disorders as an adult?," Social Science & Medicine, Elsevier, vol. 151(C), pages 78-91.
    2. Flepp, Raphael & Nüesch, Stephan & Franck, Egon, 2017. "The liquidity advantage of the quote-driven market: Evidence from the betting industry," The Quarterly Review of Economics and Finance, Elsevier, vol. 64(C), pages 306-317.
    3. Johanna Catherine Maclean & Lauren Hersch Nicholas & Keshar M. Ghimire, 2017. "The Impact of State Medical Marijuana Laws on Social Security Disability Insurance and Workers' Compensation Benefit Claiming," Working Papers id:12111, eSocialSciences.
    4. Kim, Jinho, 2016. "Personality traits and body weight: Evidence using sibling comparisons," Social Science & Medicine, Elsevier, vol. 163(C), pages 54-62.

    More about this item

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I19 - Health, Education, and Welfare - - Health - - - Other

    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:nbr:nberwo:18252. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: () or (Joanne Lustig). General contact details of provider: http://edirc.repec.org/data/nberrus.html .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.