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Arbitrarily Normalized Coefficients, Information Sets, and False Reports of "Biases" in Binary Outcome Models

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  • Thomas A. Mroz

    (Department of Economics, Clemson University)

  • Yaraslau V. Zayats

    (Bates White, LLC.)

Abstract

Empirical researchers sometimes misinterpret how additional regressors, heterogeneity corrections, and multilevel factors impact the interpretation of the estimated parameters in binary outcome models such as logit and probit. This can result in incorrect inferences about the importance of incorporating such features in these nonlinear statistical models. Some reports of biases in binary outcome models appear related to the arbitrary variance normalization required in binary outcome models. A focus on readily interpretable numerical quantities, rather than conveniently chosen "effects" as measured by arbitrarily scaled coefficients, would eliminate nearly all of the interpretation problems we highlight in this paper. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • 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.
  • Handle: RePEc:tpr:restat:v:90:y:2008:i:3:p:406-413
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    Cited by:

    1. Nicoletti, Cheti & Rondinelli, Concetta, 2006. "The (mis)specification of discrete time duration models with unobserved heterogenity: a Monte Carlo study," ISER Working Paper Series 2006-53, Institute for Social and Economic Research.
    2. Dana Goldman & Nicole Maestas, 2013. "Medical Expenditure Risk And Household Portfolio Choice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 527-550, June.
    3. Hotchkiss, Julie L., 2014. "Adjusted Employment-to-Population Ratio as an Indicator of Labor Market Strength," FRB Atlanta Working Paper 2014-8, Federal Reserve Bank of Atlanta.
    4. Li, Xianghong & Smith, Barry, 2015. "Diagnostic analysis and computational strategies for estimating discrete time duration models—A Monte Carlo study," Journal of Econometrics, Elsevier, vol. 187(1), pages 275-292.
    5. Hess, Wolfgang & Persson, Maria, 2010. "The Duration of Trade Revisited. Continuous-Time vs. Discrete-Time Hazards," Working Papers 2010:1, Lund University, Department of Economics.
    6. Islam, Towhidul & Meade, Nigel, 2013. "The impact of attribute preferences on adoption timing: The case of photo-voltaic (PV) solar cells for household electricity generation," Energy Policy, Elsevier, vol. 55(C), pages 521-530.
    7. Hess , Wolfgang & Schwarzkopf , Larissa & Hunger , Matthias & Holle , Rolf, 2013. "Competing-Risks Duration Models with Correlated Random Effects: An Application to Dementia Patients’ Transition Histories," Working Papers 2013:28, Lund University, Department of Economics.
    8. Mircea Trandafir, 2014. "The Effect of Same-Sex Marriage Laws on Different-Sex Marriage: Evidence From the Netherlands," Demography, Springer;Population Association of America (PAA), vol. 51(1), pages 317-340, February.
    9. Edward C. Norton, 2012. "Log Odds and Ends," NBER Working Papers 18252, National Bureau of Economic Research, Inc.
    10. Hess, Wolfgang & Persson, Maria, 2009. "Survival and Death in International Trade - Discrete-Time Durations of EU Imports," Working Papers 2009:12, Lund University, Department of Economics.
    11. Wolfgang Hess & Maria Persson, 2012. "The duration of trade revisited," Empirical Economics, Springer, pages 1083-1107.
    12. Cvrcek, Tomas, 2012. "America's settling down: How better jobs and falling immigration led to a rise in marriage, 1880–1930," Explorations in Economic History, Elsevier, vol. 49(3), pages 335-351.

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