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Removing Specification Errors from the Usual Formulation of Binary Choice Models

Author

Listed:
  • P.A.V.B. Swamy

    (Federal Reserve Board (Retired), Washington, DC 20551, USA
    Current Address: 6333 Brocketts Crossing, Kingstowne, VA 22315, USA)

  • I-Lok Chang

    (Department of Mathematics (Retired), American University, Washington, DC 20016, USA)

  • Jatinder S. Mehta

    (Department of Mathematics (Retired), Temple University, Philadelphia, PA 19122, USA)

  • William H. Greene

    (Department of Economics, New York University, 44 West Fourth Street, 7-90 New York, NY 10012, USA)

  • Stephen G. Hall

    (Leicester University, Room Astley Clarke 116, University Road, Leicester LEI 7RH, UK
    Bank of Greece, 21 El. Venizelos Ave., 10250 Athens, Greece)

  • George S. Tavlas

    (Leicester University, Room Astley Clarke 116, University Road, Leicester LEI 7RH, UK
    Monetary Policy Council, Bank of Greece, 21 El. Venizelos Ave., Athens 10250, Greece)

Abstract

We develop a procedure for removing four major specification errors from the usual formulation of binary choice models. The model that results from this procedure is different from the conventional probit and logit models. This difference arises as a direct consequence of our relaxation of the usual assumption that omitted regressors constituting the error term of a latent linear regression model do not introduce omitted regressor biases into the coefficients of the included regressors.

Suggested Citation

  • P.A.V.B. Swamy & I-Lok Chang & Jatinder S. Mehta & William H. Greene & Stephen G. Hall & George S. Tavlas, 2016. "Removing Specification Errors from the Usual Formulation of Binary Choice Models," Econometrics, MDPI, vol. 4(2), pages 1-21, June.
    • Handle: RePEc:gam:jecnmx:v:4:y:2016:i:2:p:26-:d:71425
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    References listed on IDEAS

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    1. Basmann, R. L., 1988. "Causality tests and observationally equivalent representations of econometric models," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 69-104.
    2. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    3. Heckman, James J. & Schmierer, Daniel, 2010. "Tests of hypotheses arising in the correlated random coefficient model," Economic Modelling, Elsevier, vol. 27(6), pages 1355-1367, November.
    4. Jun Ma & Mark Wohar (ed.), 2014. "Recent Advances in Estimating Nonlinear Models," Springer Books, Springer, edition 127, number 978-1-4614-8060-0, December.
    5. P.A.V.B. Swamy & George S. Tavlas & Stephen G. Hall, 2015. "On the Interpretation of Instrumental Variables in the Presence of Specification Errors," Econometrics, MDPI, vol. 3(1), pages 1-10, January.
    6. Swamy, P. A. V. B. & Von Zur Muehlen, Peter, 1988. "Further thoughts on testing for causality with econometric models," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 105-147.
    7. Berenguer Rico, Vanessa & Gonzalo, Jesús, 2011. "Summability of stochastic processes: a generalization of integration and co-integration valid for non-linear processes," UC3M Working papers. Economics we1115, Universidad Carlos III de Madrid. Departamento de Economía.
    8. Pratt, John W. & Schlaifer, Robert, 1988. "On the interpretation and observation of laws," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 23-52.
    9. Jesus Felipe & Franklin M. Fisher, 2003. "Aggregation in Production Functions: What Applied Economists should Know," Metroeconomica, Wiley Blackwell, vol. 54(2‐3), pages 208-262, May.
    10. White, Halbert, 1980. "Using Least Squares to Approximate Unknown Regression Functions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(1), pages 149-170, February.
    11. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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    Cited by:

    1. P.A.V.B. Swamy & Jatinder S. Mehta & I-Lok Chang, 2017. "Endogeneity, Time-Varying Coefficients, and Incorrect vs. Correct Ways of Specifying the Error Terms of Econometric Models," Econometrics, MDPI, vol. 5(1), pages 1-17, February.

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    More about this item

    Keywords

    binary choice models; specification errors; stochastic coefficients;
    All these keywords.

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C - Mathematical and Quantitative Methods
    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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