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Global Identification of the Semiparametric Box-Cox Model

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  • Komunjer, Ivana

Abstract

This paper establishes the identifiability of the parameters of the Box-Cox model under restrictions that do not require the disturbance in the model to be independent of the explanatory variables. The proposed restrictions are semiparametric in nature: they restrict the support of the conditional distribution of the disturbance but do not require the latter to be known.

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  • Komunjer, Ivana, 2008. "Global Identification of the Semiparametric Box-Cox Model," University of California at San Diego, Economics Working Paper Series qt97s197d4, Department of Economics, UC San Diego.
    • Handle: RePEc:cdl:ucsdec:qt97s197d4
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    References listed on IDEAS

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    1. Amemiya, Takeshi & Powell, James L., 1981. "A comparison of the Box-Cox maximum likelihood estimator and the non-linear two-stage least squares estimator," Journal of Econometrics, Elsevier, vol. 17(3), pages 351-381, December.
    2. N.E. Savin & Allan H. Würtz, 2002. "Testing the Semiparametric Box-Cox Model with Bootstrap," CAM Working Papers 2002-08, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics.
    3. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-591, May.
    4. Powell, James L., 1996. "Rescaled methods-of-moments estimation for the Box-Cox regression model," Economics Letters, Elsevier, vol. 51(3), pages 259-265, June.
    5. Khazzoom, J. Daniel, 1989. "A note on the application of the nonlinear two-stage least-squares estimator to a Box-Cox-transformed model," Journal of Econometrics, Elsevier, vol. 42(3), pages 377-379, November.
    6. Komunjer, Ivana, 2008. "Global Identification In Nonlinear Semiparametric Models," University of California at San Diego, Economics Working Paper Series qt2r59d87f, Department of Economics, UC San Diego.
    7. Foster A. M. & Tian L. & Wei L. J., 2001. "Estimation for the Box-Cox Transformation Model Without Assuming Parametric Error Distribution," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1097-1101, September.
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